Image analysis using a semiconductor processor for facial evaluation in vehicles

ABSTRACT

Analysis for convolutional processing is performed using logic encoded in a semiconductor processor. The semiconductor chip evaluates pixels within an image of a person in a vehicle, where the analysis identifies a facial portion of the person. The facial portion of the person can include facial landmarks or regions. The semiconductor chip identifies one or more facial expressions based on the facial portion. The facial expressions can include a smile, frown, smirk, or grimace. The semiconductor chip classifies the one or more facial expressions for cognitive response content. The semiconductor chip evaluates the cognitive response content to produce cognitive state information for the person. The semiconductor chip enables manipulation of the vehicle based on communication of the cognitive state information to a component of the vehicle.

RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplications “Image Analysis for Human Perception ArtificialIntelligence” Ser. No. 62/827,088, filed Mar. 31, 2019, “VehicleInterior Object Management” Ser. No. 62/893,298, filed Aug. 29, 2019,“Deep Learning In Situ Retraining” Ser. No. 62/925,990, filed Oct. 25,2019, and “Data Versioning for Neural Network Training” Ser. No.62/926,009, filed Oct. 25, 2019.

This application is also a continuation-in-part of “Image Analysis Usinga Semiconductor Processor for Facial Evaluation” Ser. No. 14/947,789,filed Nov. 20, 2015, which claims the benefit of U.S. provisional patentapplications “Semiconductor Based Mental State Analysis” Ser. No.62/082,579, filed Nov. 20, 2014, “Viewership Analysis Based on FacialEvaluation” Ser. No. 62/128,974, filed Mar. 5, 2015, “Mental State EventSignature Usage” Ser. No. 62/217,872, filed Sep. 12, 2015, and “ImageAnalysis In Support of Robotic Manipulation” Ser. No. 62/222,518, filedSep. 23, 2015.

The U.S. patent application “Image Analysis Using a SemiconductorProcessor for Facial Evaluation” Ser. No. 14/947,789, filed Nov. 20,2015 is also a continuation-in-part of U.S. patent application “MentalState Analysis Using Web Services” Ser. No. 13/153,745, filed Jun. 6,2011, which claims the benefit of U.S. provisional patent applications“Mental State Analysis Through Web Based Indexing” Ser. No. 61/352,166,filed Jun. 7, 2010, “Measuring Affective Data for Web-EnabledApplications” Ser. No. 61/388,002, filed Sep. 30, 2010, “Sharing AffectAcross a Social Network” Ser. No. 61/414,451, filed Nov. 17, 2010,“Using Affect Within a Gaming Context” Ser. No. 61/439,913, filed Feb.6, 2011, “Recommendation and Visualization of Affect Responses toVideos” Ser. No. 61/447,089, filed Feb. 27, 2011, “Video Ranking Basedon Affect” Ser. No. 61/447,464, filed Feb. 28, 2011, and “Baseline FaceAnalysis” Ser. No. 61/467,209, filed Mar. 24, 2011.

The U.S. patent application “Image Analysis Using a SemiconductorProcessor for Facial Evaluation” Ser. No. 14/947,789, filed Nov. 20,2015 is also a continuation-in-part of U.S. patent application “MentalState Analysis Using an Application Programming Interface” Ser. No.14/460,915, Aug. 15, 2014, which claims the benefit of U.S. provisionalpatent applications “Application Programming Interface for Mental StateAnalysis” Ser. No. 61/867,007, filed Aug. 16, 2013, “Mental StateAnalysis Using an Application Programming Interface” Ser. No.61/924,252, filed Jan. 7, 2014, “Heart Rate Variability Evaluation forMental State Analysis” Ser. No. 61/916,190, filed Dec. 14, 2013, “MentalState Analysis for Norm Generation” Ser. No. 61/927,481, filed Jan. 15,2014, “Expression Analysis in Response to Mental State Express Request”Ser. No. 61/953,878, filed Mar. 16, 2014, “Background Analysis of MentalState Expressions” Ser. No. 61/972,314, filed Mar. 30, 2014, and “MentalState Event Definition Generation” Ser. No. 62/023,800, filed Jul. 11,2014.

The U.S. patent application “Mental State Analysis Using an ApplicationProgramming Interface” Ser. No. 14/460,915, Aug. 15, 2014 is also acontinuation-in-part of U.S. patent application “Mental State AnalysisUsing Web Services” Ser. No. 13/153,745, filed Jun. 6, 2011, whichclaims the benefit of U.S. provisional patent applications “Mental StateAnalysis Through Web Based Indexing” Ser. No. 61/352,166, filed Jun. 7,2010, “Measuring Affective Data for Web-Enabled Applications” Ser. No.61/388,002, filed Sep. 30, 2010, “Sharing Affect Across a SocialNetwork” Ser. No. 61/414,451, filed Nov. 17, 2010, “Using Affect Withina Gaming Context” Ser. No. 61/439,913, filed Feb. 6, 2011,“Recommendation and Visualization of Affect Responses to Videos” Ser.No. 61/447,089, filed Feb. 27, 2011, “Video Ranking Based on Affect”Ser. No. 61/447,464, filed Feb. 28, 2011, and “Baseline Face Analysis”Ser. No. 61/467,209, filed Mar. 24, 2011.

The U.S. patent application “Image Analysis Using a SemiconductorProcessor for Facial Evaluation” Ser. No. 14/947,789, filed Nov. 20,2015 is also a continuation-in-part of U.S. patent application “MentalState Evaluation Learning for Advertising” Ser. No. 13/708,027, Dec. 7,2012, which claims the benefit of U.S. provisional patent applications“Mental State Evaluation Learning for Advertising” Ser. No. 61/568,130,filed Dec. 7, 2011 and “Affect Based Evaluation of AdvertisementEffectiveness” Ser. No. 61/581,913, filed Dec. 30, 2011.

The U.S. patent application “Mental State Evaluation Learning forAdvertising” Ser. No. 13/708,027, Dec. 7, 2012 is also acontinuation-in-part of U.S. patent application “Mental State AnalysisUsing Web Services” Ser. No. 13/153,745, filed Jun. 6, 2011 which claimsthe benefit of U.S. provisional patent applications “Mental StateAnalysis Through Web Based Indexing” Ser. No. 61/352,166, filed Jun. 7,2010, “Measuring Affective Data for Web-Enabled Applications” Ser. No.61/388,002, filed Sep. 30, 2010, “Sharing Affect Data Across a SocialNetwork” Ser. No. 61/414,451, filed Nov. 17, 2010, “Using Affect Withina Gaming Context” Ser. No. 61/439,913, filed Feb. 6, 2011,“Recommendation and Visualization of Affect Responses to Videos” Ser.No. 61/447,089, filed Feb. 27, 2011, “Video Ranking Based on Affect”Ser. No. 61/447,464, filed Feb. 28, 2011, and “Baseline Face Analysis”Ser. No. 61/467,209, filed Mar. 24, 2011.

This application is also a continuation-in-part of U.S. patentapplication “Vehicle Manipulation Using Cognitive State Engineering”Ser. No. 16/429,022, filed Jun. 2, 2019, which claims the benefit ofU.S. provisional patent applications “Vehicle Manipulation UsingCognitive State Engineering” Ser. No. 62/679,825, filed Jun. 3, 2018,and “Image Analysis for Human Perception Artificial Intelligence” Ser.No. 62/827,088, filed Mar. 31, 2019.

The U.S. patent application “Vehicle Manipulation Using Cognitive StateEngineering” Ser. No. 16/429,022, filed Jun. 2, 2019 is also acontinuation-in-part of U.S. patent application “Vehicle Manipulationusing Occupant Image Analysis” Ser. No. 15/875,644, filed Jan. 19, 2018,which claims the benefit of U.S. provisional patent applications“Vehicle Manipulation using Occupant Image Analysis” Ser. No.62/448,448, filed Jan. 20, 2017, “Image Analysis for Two-sided Data Hub”Ser. No. 62/469,591, filed Mar. 10, 2017, “Vehicle ArtificialIntelligence Evaluation of Mental States” Ser. No. 62/503,485, filed May9, 2017, “Image Analysis for Emotional Metric Generation” Ser. No.62/524,606, filed Jun. 25, 2017, “Image Analysis and Representation forEmotional Metric Threshold Evaluation” Ser. No. 62/541,847, filed Aug.7, 2017, “Multimodal Machine Learning for Emotion Metrics” Ser. No.62/557,460, filed Sep. 12, 2017, “Speech Analysis for Cross-LanguageMental State Identification” Ser. No. 62/593,449, filed Dec. 1, 2017,“Avatar Image Animation using Translation Vectors” Ser. No. 62/593,440,filed Dec. 1, 2017, and “Directed Control Transfer for AutonomousVehicles” Ser. No. 62/611,780, filed Dec. 29, 2017.

The U.S. patent application “Vehicle Manipulation using Occupant ImageAnalysis” Ser. No. 15/875,644, filed Jan. 19, 2018, is also acontinuation-in-part of U.S. patent application “Image Analysis inSupport of Robotic Manipulation” Ser. No. 15/273,765, filed Sep. 23,2016, which claims the benefit of U.S. provisional patent applications“Image Analysis In Support of Robotic Manipulation” Ser. No. 62/222,518,filed Sep. 23, 2015, “Analysis of Image Content with AssociatedManipulation of Expression Presentation” Ser. No. 62/265,937, filed Dec.12, 2015, “Image Analysis Using Sub-Sectional Component Evaluation ToAugment Classifier Usage” Ser. No. 62/273,896, filed Dec. 31, 2015,“Analytics for Live Streaming Based on Image Analysis within a SharedDigital Environment” Ser. No. 62/301,558, filed Feb. 29, 2016, and “DeepConvolutional Neural Network Analysis of Images for Mental States” Ser.No. 62/370,421, filed Aug. 3, 2016.

The U.S. patent application “Image Analysis in Support of RoboticManipulation” Ser. No. 15/273,765, filed Sep. 23, 2016 is acontinuation-in-part of U.S. patent application “Mental State EventDefinition Generation” Ser. No. 14/796,419, filed Jul. 10, 2015 whichclaims the benefit of U.S. provisional patent applications “Mental StateEvent Definition Generation” Ser. No. 62/023,800, filed Jul. 11, 2014,“Facial Tracking with Classifiers” Ser. No. 62/047,508, filed Sep. 8,2014, “Semiconductor Based Mental State Analysis” Ser. No. 62/082,579,filed Nov. 20, 2014, and “Viewership Analysis Based On FacialEvaluation” Ser. No. 62/128,974, filed Mar. 5, 2015.

The U.S. patent application “Mental State Event Definition Generation”Ser. No. 14/796,419, filed Jul. 10, 2015 is also a continuation-in-partof U.S. patent application “Mental State Analysis Using Web Services”Ser. No. 13/153,745, filed Jun. 6, 2011, which claims the benefit ofU.S. provisional patent applications “Mental State Analysis Through WebBased Indexing” Ser. No. 61/352,166, filed Jun. 7, 2010, “MeasuringAffective Data for Web-Enabled Applications” Ser. No. 61/388,002, filedSep. 30, 2010, “Sharing Affect Across a Social Network” Ser. No.61/414,451, filed Nov. 17, 2010, “Using Affect Within a Gaming Context”Ser. No. 61/439,913, filed Feb. 6, 2011, “Recommendation andVisualization of Affect Responses to Videos” Ser. No. 61/447,089, filedFeb. 27, 2011, “Video Ranking Based on Affect” Ser. No. 61/447,464,filed Feb. 28, 2011, and “Baseline Face Analysis” Ser. No. 61/467,209,filed Mar. 24, 2011.

The U.S. patent application “Mental State Event Definition Generation”Ser. No. 14/796,419, filed Jul. 10, 2015 is also a continuation-in-partof U.S. patent application “Mental State Analysis Using an ApplicationProgramming Interface” Ser. No. 14/460,915, Aug. 15, 2014, which claimsthe benefit of U.S. provisional patent applications “ApplicationProgramming Interface for Mental State Analysis” Ser. No. 61/867,007,filed Aug. 16, 2013, “Mental State Analysis Using an ApplicationProgramming Interface” Ser. No. 61/924,252, filed Jan. 7, 2014, “HeartRate Variability Evaluation for Mental State Analysis” Ser. No.61/916,190, filed Dec. 14, 2013, “Mental State Analysis for NormGeneration” Ser. No. 61/927,481, filed Jan. 15, 2014, “ExpressionAnalysis in Response to Mental State Express Request” Ser. No.61/953,878, filed Mar. 16, 2014, “Background Analysis of Mental StateExpressions” Ser. No. 61/972,314, filed Mar. 30, 2014, and “Mental StateEvent Definition Generation” Ser. No. 62/023,800, filed Jul. 11, 2014.

The U.S. patent application “Mental State Event Definition Generation”Ser. No. 14/796,419, filed Jul. 10, 2015 is also a continuation-in-partof U.S. patent application “Mental State Analysis Using Web Services”Ser. No. 13/153,745, filed Jun. 6, 2011, which claims the benefit ofU.S. provisional patent applications “Mental State Analysis Through WebBased Indexing” Ser. No. 61/352,166, filed Jun. 7, 2010, “MeasuringAffective Data for Web-Enabled Applications” Ser. No. 61/388,002, filedSep. 30, 2010, “Sharing Affect Across a Social Network” Ser. No.61/414,451, filed Nov. 17, 2010, “Using Affect Within a Gaming Context”Ser. No. 61/439,913, filed Feb. 6, 2011, “Recommendation andVisualization of Affect Responses to Videos” Ser. No. 61/447,089, filedFeb. 27, 2011, “Video Ranking Based on Affect” Ser. No. 61/447,464,filed Feb. 28, 2011, and “Baseline Face Analysis” Ser. No. 61/467,209,filed Mar. 24, 2011.

Each of the foregoing applications is hereby incorporated by referencein its entirety.

FIELD OF ART

This application relates generally to analysis of images and moreparticularly to image analysis using a semiconductor processor forfacial evaluation in vehicles.

BACKGROUND

On any given day, an individual experiences various external stimuli,which can provoke a wide range of responses. The responses of theindividual can manifest in cognitive states, mental or emotional states,facial expressions, body language, and so on. The stimuli are perceivedthrough one or more senses and can be visual, aural, olfactory, tactile,and so on. The stimuli, whether alone or in combination, can evokestrong cognitive states or emotions in the individual who experiencesthose stimuli. Not all individuals in the presence of the variousstimuli will react in a similar manner. That is, when a group ofindividuals experiences the stimuli, the reactions of the individualscan be at times substantially similar, at other times widely dissimilar,and so on. How an individual reacts to experienced stimuli can beimportant to defining the essence of that individual. Furthermore, theresponses of the individual to the stimuli can have a profound impact onthe cognitive states experienced by that individual.

The cognitive states that an individual can experience in response toexternal stimuli can vary depending on time frames. The time framescould be one time of day versus another, one day of the week or monthversus another, and so on. An individual's cognitive state contributesto general well-being. Cognitive state further impacts her or hisperception of the surrounding environment, decision-making processes,and so on. The cognitive states of multiple individuals that result froma common event can carry a collective importance. At times thecollective importance of the event can be more impactful than theimportance of each individual's cognitive state. The cognitive states ofan individual or a group of individuals can vary widely, ranging fromhappiness to sadness, from contentedness to worry, and from calm toexcitement, to name only a few possible states. Despite how critical andinfluential one's cognitive states are to daily life, the cognitivestate of a single individual, let alone those of a group, might notalways be apparent, even to that individual. The ability and means bywhich one person perceives her or his cognitive state can be quitedifficult to express or summarize. Though an individual can oftenperceive her or his own emotional state quickly, instinctively, and withlittle or no conscious effort, the individual might encounter difficultywhen attempting to summarize or communicate her or his cognitive stateto others. This difficulty of understanding and communicating cognitivestates becomes far more complex when the cognitive states of multipleindividuals are considered.

Gaining an insight into the cognitive states of one or more individualsis an important technique for understanding how people react to variousexternal stimuli. Those external stimuli can include views of thenatural landscape, political and sports events, educational programs,natural disasters, etc. However, proper interpretation of cognitivestates is very difficult when the individuals being considered arethemselves unable to accurately identify and communicate their cognitivestates. The identification and communication of cognitive states can befurther complicated by the fact that multiple individuals can havesimilar or very different cognitive states when taking part in acommunal activity. The cognitive states of two friends viewing animportant political debate can be disparate. If one friend is asupporter of the winning candidate, while the other friend is asupporter of the losing candidate, it is reasonable to expect widelyvarying cognitive states between the two friends. The problem ofdefining the resulting cognitive states from multiple peopleexperiencing complex stimuli can be a considerably complicated exercise.

SUMMARY

Modern electronic devices are constructed with a variety ofspecial-purpose hardware that is integral to the operation of thedevices. This special-purpose hardware enables the devices to support avariety of additional functions. A device such as a typical smart phoneincludes not only the battery, radios, and keyboard required to supporttelephony, SMS (text), and other common features, but also cameras,displays, haptic input devices, accelerometers, global positioningsystems (GPS), audio codecs, microphones, and so on. The inclusion ofthis special-purpose hardware into the device vastly expands thecapabilities and usefulness of the electronic devices by enabling thedevices to support mapping, positioning, video communications, socialnetworking, etc. As additional hardware is added to the electronicdevices, new and emerging capabilities further expand the usefulness ofthe devices. Traveling in vehicles, using social media, and so on, areareas that can take advantage of many of the features and capabilitiesafforded by the special-purpose hardware. The GPS can be used forplanning routes or locating traffic problems, or for finding friends ora favorite eatery. The display is useful for showing driving directionsor for sharing pictures and videos, and so on. The cameras are used forobserving operators or passengers within vehicles or making video callsand chats. Analyzing images of a person in a vehicle can also give asense of a person's disposition to the transportation experience withina vehicle, reaction to travel conditions such as traffic or weather,mental or cognitive state such as alert or distracted, and so on. Logicdevices can be used to analyze data including video data andphysiological data. The data can be collected using cameras, sensors,accelerometers, and so on. When the collected data includes videos,video segments, still images, etc., then the video data in turn can beanalyzed for a facial portion of one or more persons. Classifiers can beapplied to facial expressions identified in the images for cognitiveresponse content. The cognitive response content is scored to producecognitive state information for a person in the image. The cognitivestate information is communicated to a component of the vehicle in orderto manipulate the vehicle.

Computational processing enables image analysis for facial evaluation invehicles. An apparatus for analysis is described comprising: a devicecontaining convolutional processing logic encoded in a semiconductorchip comprising: evaluation logic trained to analyze pixels within animage of a person in a vehicle, wherein the analysis identifies a facialportion of the person; identification logic trained to identify one ormore facial expressions based on the facial portion; classifying logictrained to classify the one or more facial expressions for cognitiveresponse content; scoring logic trained to evaluate the cognitiveresponse content to produce cognitive state information for the person;and interface logic that enables manipulation of the vehicle based oncommunication of the cognitive state information to a component of thevehicle. Further, an additional facial portion from an image of anadditional person within the vehicle is evaluated, identified,classified, and scored to produce additional cognitive state informationfor the additional person. The cognitive state information is augmentedbased on audio data or physiological data collected from within thevehicle, wherein the audio data or the physiological data is collectedcontemporaneously with the image.

Embodiments include a computer program product embodied in anon-transitory computer readable medium for image analysis, the computerprogram product comprising: code for executing on a device containing aconvolutional processing logic encoded in a semiconductor chipcomprising: evaluation logic trained to analyze pixels within an imageof a person in a vehicle, wherein the analysis identifies a facialportion of the person; identification logic trained to identify one ormore facial expressions based on the facial portion; classifying logictrained to classify the one or more facial expressions for cognitiveresponse content; scoring logic trained to evaluate the cognitiveresponse content to produce cognitive state information for the person;and interface logic that enables manipulation of the vehicle based oncommunication of the cognitive state information to a component of thevehicle.

Some embodiments include a processor-implemented method for analysiscomprising: using a device containing convolutional processing logicencoded in a semiconductor chip to perform: analyzing pixels within animage of a person in a vehicle, wherein the analysis identifies a facialportion of the person; identifying one or more facial expressions basedon the facial portion; classifying the one or more facial expressionsfor cognitive response content; evaluating the cognitive responsecontent to produce cognitive state information for the person; andmanipulating the vehicle based on communication of the cognitive stateinformation to a component of the vehicle.

Various features, aspects, and advantages of various embodiments willbecome more apparent from the following further description.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description of certain embodiments may beunderstood by reference to the following figures wherein:

FIG. 1 is a system diagram for cognitive state analysis using facialevaluation in vehicles.

FIG. 2 is a flow diagram for cognitive state analysis.

FIG. 3 is a flow diagram for convolutional processing device usage.

FIG. 4 illustrates image collection for cognitive state analysis.

FIG. 5 is an example showing a second face.

FIG. 6 illustrates a semiconductor chip with classifiers.

FIG. 7 shows apps calling the semiconductor chip analysis machine.

FIG. 8 illustrates an example of live streaming of social video andaudio.

FIG. 9 shows example facial data collection including landmarks.

FIG. 10 is a flow diagram for detecting facial expressions.

FIG. 11 is a flow diagram for the large-scale clustering of facialevents.

FIG. 12 shows example unsupervised clustering of features andcharacterizations of cluster profiles.

FIG. 13A shows example tags embedded in a webpage.

FIG. 13B shows an example of invoking tags to collect images.

FIG. 14 is a system diagram for an interior of a vehicle.

FIG. 15 is a timeline with information tracks relating to cognitivestates.

FIG. 16 illustrates example image and audio collection includingmultiple mobile devices.

FIG. 17 is an example showing a convolutional neural network.

FIG. 18 illustrates a bottleneck layer within a deep learningenvironment.

FIG. 19 shows data collection including devices and locations.

FIG. 20 is a system for cognitive state analysis.

DETAILED DESCRIPTION

The proliferation of electronic devices, and handheld electronic devicesin particular, has changed the ways in which people communicate. Whilesmartphones still retain the functionality to communicate verbally,users of these and other electronic devices often choose to communicatevia other modes. Popular communication modes that device usersfrequently choose include sending SMS (text) messages, using chat (voiceand text) applications, posting on social media (e.g. Twitter™,Facebook™, Instagram™, etc.), and other “nontraditional” modes.According to some reports, electronic verbal communication has becomenontraditional in comparison to the other modes. As a result of theseuser-driven changes in device usage, a wide variety of apps has beenwritten. Further, special-purpose or custom hardware has been designedand added to the electronic devices, which greatly enhances theirfunctionality and usefulness. This special-purpose hardware supports thepopular and preferred apps and usage schemes and enables new andcreative interaction modalities. A typical smartphone today includes thebattery, radios, and keyboards required to support telephony, SMS(text), and other common features, plus cameras, displays, haptic inputdevices (3-D touch), accelerometers, global positioning systems (GPS),audio codecs, microphones, and so on. This special-purpose hardwareenables the devices to perform mapping, positioning, videocommunications, social networking, gaming, and many other functions.

The people who use the electronic devices live and work in variousregions, locations, and environments. The residences in which thesepeople live are situated in areas as diverse as densely populatedcities, sparsely populated rolling hills, open plains, woodlands, oreven aboard a boat. Irrespective of whether dwellings are located inurban, suburban, or rural areas, people spend hundreds or more hours peryear traveling. The traveling typically involves some sort of vehicle,where vehicles include public vehicles, private vehicles, and“alternative” vehicles. The most frequently used vehicles include publicbuses, trains, or airplanes; private vehicles such as automobiles ormotorcycles; commercial vehicles such as taxis or ride share vehicles;and so on. Whichever vehicle is used, traveling takes time. The hoursspent by individuals in vehicles are expended while commuting to andfrom work or school, running errands or shopping, keeping meetings andappointments, traveling, etc. The travel time erodes the opportunity formore productive pursuits such as time with friends and family, creating,or exercising.

As an individual travels, consumes media, or interacts with anelectronic or computing device, that person can experience a wide rangeof cognitive states. The cognitive states can include drowsiness,sadness, engagement, boredom, and so on. The types and ranges ofcognitive states can be determined by analyzing data, including imageswhich include facial portion data, obtained from the person while in avehicle. The obtained data that is analyzed can include data frommultiple images, angles, or light wavelengths; facial data or torsodata; audio data, voice data, speech data, or non-speech vocalizations;physiological data; and the like. The analysis can be performed usingconvolutional processing logic encoded in a semiconductor chip. Theconvolutional processing can be based on a neural network, where theneural network can be configured for deep learning. The neural networkcan be trained to analyze the obtained data and to identify one or morecognitive states. The neural network can be adapted or “retrained”, asmore data is analyzed by the network, to speed operation, to improveconvergence, and so on. The convolutional processing logic can be usedfor image analysis for facial evaluation in vehicles.

In the disclosed materials, convolutional processing logic enables imageanalysis for facial evaluation in vehicles. The convolutional processinglogic can be based on a neural network such as a convolutional neuralnetwork or recurrent neural network. The facial evaluation can be usedto determine cognitive state information associated with a person in avehicle. The cognitive state information associated with the person canbe communicated to a component of the vehicle. The communicating to thecomponent of the vehicle can enable manipulation of the vehicle. Anindividual can be observed as she or he is in the vehicle. Theindividual can be a vehicle passenger, an operator or driver, and so on.The individual can be interacting with the vehicle, with an electronicdevice or a computing device, and so on. The individual can be consumingmedia while traveling or on a vehicle. The cognitive state analysis isbased on obtaining images that include a facial portion of theindividual. The images can include video images, still images,intermittently obtained images, and so on. The images can includevisible light images, near-infrared light images, etc. Facialexpressions can be identified from the facial portions in the images,and the facial expressions can be classified. The classifying the facialexpressions can produce content such as cognitive response content. Thecognitive response content can be scored, where the scoring can producecognitive state information. The cognitive state information can includeone or more of drowsiness, fatigue, distraction, sadness, stress,happiness, anger, frustration, confusion, disappointment, hesitation,cognitive overload, focusing, engagement, attention, boredom,exploration, confidence, trust, delight, disgust, skepticism, doubt,satisfaction, excitement, laughter, calmness, curiosity, humor,depression, envy, sympathy, embarrassment, poignancy, or mirth. Thecognitive state information can be used to manipulate the vehicle.

Having obtained one or more images of a person in a vehicle, anevaluation of the images is performed. Similarly, one or more images canbe obtained from one or more other people within the vehicle using oneor more imaging devices. The image evaluations, which can be based onanalyzing pixels within an image of a person in a vehicle, can identifyfacial portions of the person. The facial portions can include faciallandmarks; regions, or regions of interest; facial characteristics; andso on. The image evaluations can provide insight into the cognitivestates of the one or more persons. All or part of the image evaluationcan take place on a portable device. Through evaluation, many differentcognitive states can be determined, including frustration, confusion,disappointment, hesitation, cognitive overload, focusing, engagement,attention, boredom, exploration, confidence, trust, delight, disgust,skepticism, doubt, satisfaction, excitement, laughter, calmness, stress,and curiosity. Other cognitive states can be determined through similarevaluations. Facial expressions can be identified based on the facialportions, and classifiers can be used to produce cognitive responsecontent. The cognitive response content is scored to produce cognitivestate information. The cognitive state information can be used tomanipulate the vehicle by communicating the cognitive state informationto the vehicle. The cognitive state information is used to adjust ormanipulate a component of the vehicle.

FIG. 1 is a system diagram for cognitive state analysis using facialevaluation in vehicles. A system 100 describes an apparatus forconvolutional processing. The system 100 includes evaluation logic 120trained to analyze pixels within an image of a person in a vehicle,wherein the analysis identifies a facial portion of the person. Thesemiconductor chip can include a standalone chip, a subsystem of a chip,a module of a multi-chip module (MCM), and so on. The semiconductor chipcan include a programmable chip such as a programmable logic array(PLA), a programmable logic device (PLD), a field programmable gatearray (FPGA), a read only memory (ROM), and so on. The semiconductorchip can include a full-custom chip design. The semiconductor chip canbe reprogrammed, reconfigured, etc., “on the fly”, in the field, or atany time which is convenient to the user of the semiconductor chip. Thesemiconductor chip can be implemented in any semiconductor technology.The evaluating of facial portions can include evaluating faces in animage, where the image can be a still image, a video, a video clip, aframe from a video, and so on. The evaluating facial portions caninclude scaling, rotating, translating, etc., faces within an image. Inembodiments, a series of images can be supplied to the device, whereinthe series of images is sourced from a video stream. The video streamcan be provided by a camera or other image capture device in thevehicle.

The semiconductor chip 110 includes identification logic 125 trained toidentify one or more facial expressions based on the facial portion. Thefacial expressions can include one or more of a smile, frown, scowl,grimace, smirk, and so on. The one or more facial expressions can conveynonverbal communication from the person. The facial portion can be basedon facial landmarks such as corners of eyes or mouth, nose, ears, etc.;facial regions such as eyebrows, eyes, nose, mouth, ears, and so on;facial features such as hair obscuring facial landmarks or regions,facial hair, prosthetics such as glasses; and the like. The identifyingfacial expressions can be based on identifying differences in facialportions, where the facial portions can include portions of interest.The identification of differences in facial portions can be based on ahistogram of gradient (HoG) evaluation. Any data representationtechnique can be used. The identification of differences can includeevaluation of facial portion locations such as eyebrow locations. Theevaluated eyebrow locations can be used to determine eyebrow raises,eyebrow furls, and so on. The identification of differences can includeevaluation of eye locations. The evaluated eye locations can be used toset landmarks for eyes within the previously located face. The eyelocations can be used to track eye movements, eye direction, gazedirection, head turns, head tilts, etc. The identification ofdifferences can include evaluation of mouth locations. The evaluatedmouth locations can be used to determine expressions including smiles,frowns, neutral expressions, and so on. The identification ofdifferences can include landmark detection within the face. The detectedfacial landmarks can include an outer edge of a nostril, the border ofthe lips, the corners of the mouth, a midpoint between the eyes, etc. AGabor filter can be utilized in the identification of differences. TheGabor filter can be used to detect edges, where the edges can includeedges of regions of interest within the face, for example.

The semiconductor chip 110 includes classifier logic 130 trained toclassify the one or more facial expressions for cognitive responsecontent. The classifying can be based on using one or more classifiers,where the classifiers can be encoded in the semiconductor chip, loadedby a user of the chip, downloaded from a library or repository, and soon. The classifiers can be used to classify facial expressions intocognitive states, mental states, emotional states, moods, and so on. Theclassifiers can be based on statistical classifiers that includeBayesian classifiers. In embodiments, the classifiers can be lightclassifiers. Light classifiers can perform some classification on thesemiconductor chip and work in coordination with off-chip hardware,off-chip software, and so on. In a usage example, classifiers used bythe classifier logic can be used to perform all, little, or noclassification on-chip, and the semiconductor chip can work incoordination with a further processor. The further processor can includea server, a cloud-based service, a distributed or mesh service, and thelike. A server, for example, can be used to perform some or all of theimage analysis including image analysis using classifiers. Any analysisby the server can be performed in real-time, at a later time, and so on.The cognitive states can include one or more of stress, sadness, anger,happiness, frustration, confusion, disappointment, hesitation, cognitiveoverload, focusing, engagement, attention, boredom, exploration,confidence, trust, delight, disgust, skepticism, doubt, satisfaction,excitement, laughter, calmness, and curiosity. The classifier logic canidentify deviations from a baseline facial expression. The deviationsfrom a baseline facial expression can indicate an individual's emotionsand cognitive states. For example, suppose that several faces arelocated within a video. In this case, a deviation can include adifference in the facial expression of one face in comparison to theexpressions of the other faces. However, note that a deviation can alsoinclude differences in the same facial expression. For example, thedeviation can include an intensity of a smile or frown that differs by aset magnitude from a predetermined baseline.

The semiconductor chip 110 includes scoring logic 135 trained toevaluate the cognitive response content to produce cognitive stateinformation for the person. The cognitive state information can includea cognitive state score based on the facial portion. The cognitive statescore can be based on reactions of the person to a series of mediapresentations rendered with the vehicle. The cognitive state score canbe based on events that occur within the vehicle or beyond the vehicle.In-vehicle events can include the media presentations, communicationbetween or among people in the vehicle, and the like. Beyond-vehicleevents can include traffic or construction delays, weather events, etc.The cognitive state score can be based on the alignment of responses toa baseline, including attunement to social norms. The cognitive statescore can be based on various parameters including self-awareness,social skill, empathy, and so on. The cognitive state score can includea cognitive state, an intensity and duration of the cognitive state, andso on. The cognitive state score can provide information on happiness oranother cognitive state based on the regions of the face, including amouth, such as where the mouth is smiling. Similarly, the cognitivestate score can provide information on other cognitive states includingsadness, agitation, irritation, confusion, distraction, impairment, andso on. The cognitive state score can provide information onconcentration based on the regions of the face, including eyebrows, suchas where the eyebrows are furrowed. The cognitive state score canprovide information on further cognitive states, including surprise,based on the eyebrows being raised. The device can further performsmoothing of the cognitive state score or information.

Scoring the cognitive response content can include capturing importantpatterns in the cognitive state information. The device can furtherperform image correction for the videos including one or more oflighting correction, contrast correction, or noise filtering. The imagecorrection can be based on a variety of signal and image processingtechniques including high-pass filtering, low-pass filtering,cross-correlation, etc. The cognitive state information can be augmentedby audio data or physiological data. The audio data can include voicedata, non-verbal vocalization data, ambient sounds within or outside ofthe vehicle, etc. The physiological data can include heart rate, heartrate variability, and so on. The physiological data can be gleaned fromthe videos of the one or more persons. The physiological data can beextracted, inferred, etc. The physiological data can also be gleanedfrom one or more biosensors. The biosensors can be attached to the oneor more individuals and can detect physiological parameters of theindividuals including heart rate, heart rate variability, respirationrate, skin temperature, skin conductivity, and so on. The cognitivestate information can be used to track one or more cognitive states. Thecognitive states can include one or more of drowsiness, fatigue,distraction, sadness, stress, happiness, anger, frustration, confusion,disappointment, hesitation, cognitive overload, focusing, engagement,attention, boredom, exploration, confidence, trust, delight, disgust,skepticism, doubt, satisfaction, excitement, laughter, calmness,curiosity, humor, depression, envy, sympathy, embarrassment, poignancy,or mirth. In some embodiments, the scoring logic further extracts one ormore histogram-of-gradient (HoG) features from the regions of interest(RoI). In embodiments, the scoring logic produces the cognitive stateinformation based on the histogram-of-gradient features.

The semiconductor chip includes interface logic 140 that enablesmanipulation of the vehicle based on communication of the cognitivestate information to a component of the vehicle. The interface logic cancomprise a register to be read directly or indirectly. The interfacelogic can comprise an output driver or bank of output drivers. Theinterface logic can comprise a more sophisticated communicationinterface. The communicating can be accomplished using wired or wirelesstechniques. In embodiments, the wireless techniques that can be usedinclude Wi-Fi, Bluetooth®, ZigBee™, etc., and the wired techniques thatcan be used include Ethernet, RS-242, IEEE488™, etc. In embodiments,fiber optic transmission is used. The manipulating the vehicle caninclude enabling manual control, autonomous control, or semiautonomouscontrol of the vehicle. The manipulating can include controlling devicesor systems within or associated with the vehicle. In embodiments, themanipulation of the vehicle includes a locking out operation;recommending a break for an occupant; recommending a different route forthe vehicle; recommending how far to drive; responding to traffic;adjusting seats, mirrors, climate control, lighting, music, audiostimuli, or interior temperature; brake activation; or steering control.

The semiconductor chip 110 includes other logic 145 that supports thesemiconductor chip. In embodiments, the other logic can provideinterface support for a variety of peripherals including one or morecameras, sensors including biosensors, storage devices such as diskdrives (hard drives, solid state drives, optical drives, etc.), memory,(RAM, ROM, CAM, etc.), displays (video, LCD, LED, OLED, etc.),input/output devices (keyboards, trackpads, mice, touch screens, audio,etc.), and so on. The other logic can include special purpose hardware,where the special purpose hardware can be configured to executealgorithms, for example. The other logic can be reconfigurable, wherethe reconfiguration can be realized by programming or reencoding. Thereconfiguration of the other logic can depend on algorithms, heuristics,control schemes, etc. that are stored in a storage device, entered bythe user, downloaded from the Internet, and so on. The other logicsupports the semiconductor chip by providing functions not supported bythe other logic blocks described above. In embodiments, the other logiccomprises categorization logic that updates a cognitive state profile ofan individual associated with the facial portion.

The semiconductor chip 110 obtains videos, video clips, images, and soon, that are streamed from a camera 150. The camera can be any type ofimage capture device and can include a webcam, a video camera, a stillcamera, a thermal imager, a CCD device, a phone camera, athree-dimensional camera, a light field camera, multiple cameras used toobtain different aspects or views of a person or multiple persons, orany other type of image capture technique that allows captured data tobe used in an electronic system. The videos and/or images can beobtained on an intermittent basis. The videos can include video framesthat can be obtained by a camera coupled to the device. The camera canbe built into the device. The camera can be coupled to the device usingwireless techniques including Wi-Fi, Bluetooth®, ZigBee™, etc., or usingwired techniques including Ethernet, RS-242, IEEE488™, etc.

The semiconductor chip 110 further includes video storage memory 160coupled to the device. The storage memory can include read-write (RW)memory, a hard disk drive (HDD), an optical drive (OD), a solid-statedisk drive (SDD), etc. The video storage memory 160 can store videos foranalysis by the device, and the analysis can be used to evaluate moods,cognitive states, facial expressions, etc., for people in the videos.The videos can be obtained from the camera 150, downloaded from anetwork, uploaded by a user, and so on. The videos can be retrieved fromthe video storage memory 160 for evaluation by the convolutional logic.The videos can be retrieved using wired and wireless means. The videostorage memory 160 can be coupled to a bus 112 of the chip, to a USBport, to a serial port, to a parallel port, or to another communicationsgateway. The video storage memory 160 can be coupled to the chip usingwireless techniques as described above.

The semiconductor chip 110 further includes classifier storage memory170 coupled to the device. As before, the classifier storage memory caninclude read-write (RW) memory, a hard disk drive (HDD), an opticaldrive (OD), a solid-state disk drive (SDD), etc. The classifier storagememory 170 can store classifiers that can be used for various processingand analysis techniques by the semiconductor device. The use of theclassifiers can help evaluate cognitive states, facial expressions,mood, emotional state, and so on, of the one or more people who can beidentified in the videos. The obtaining of the classifiers can takeplace by the classifiers being encoded in or loaded into thesemiconductor chip, entered by a user, downloaded from the Internet, andso on. The classifiers can be changed at any time by recoding,reloading, reentering, re-downloading, and so on. The classifiers can beused to reprogram, reconfigure, or otherwise change or modify the chip.For example, one or more classifiers can be used to configure the otherlogic 145, to modify the classifier logic 130, and so on. Inembodiments, the classifier storage memory stores classifier informationused by the classifier logic.

FIG. 2 is a flow diagram for cognitive state analysis. The flow 200, orportions thereof, can be implemented using convolutional processinglogic encoded in semiconductor logic. The flow 200 describes cognitivestate analysis based on analysis of captured videos of one or morepeople in a vehicle or a plurality of vehicles. The cognitive stateanalysis uses the semiconductor chip for facial evaluation in vehicles.The flow 200 includes obtaining video frames 210 that are streamed froma camera. The video frames can be obtained from a video and can includeone or more people. The camera can be any type of image capture devicecapable of capturing data to be used in an electronic system, such as awebcam, a video camera, a still camera, a thermal imager, a CCD device,a phone camera, a three-dimensional camera, a light field camera,multiple cameras to obtain different aspects or views of a person, orany other type of image capture technique. The flow 200 includes storingvideos for analysis 212 to evaluate the moods of people in the videos.The videos can be stored in any appropriate storage medium including ahard disk drive, an optical drive, a solid-state drive, and so on. Thevideos can be stored in cloud-based storage. In embodiments, the storagemedium can further include storage memory coupled to the device. Thememory coupled to the device can include memory within the semiconductorchip, removable memory such as secure digital (SD) memory, flash memory,etc. As discussed below, the storage memory can store videos foranalysis by the device to evaluate cognitive state information forpeople in the videos. The videos can be stored for later retrieval andanalysis. In some embodiments, analysis of the videos is performed priorto storage. The flow 200 includes performing image correction 214 forthe videos, including one or more of exposure or lighting correction,contrast correction, or noise filter smoothing. Other image correctionscan be performed, including highlight correction, shadow correction,saturation correction, temperature, tint, sharpness, and so on.

The flow 200 includes analyzing pixels 220 within an image of a personin a vehicle. The videos obtained of the person in the vehicle can bepartitioned into video frames, and the video frames can be analyzed. Inembodiments, a series of images can be supplied to the device, whereinthe series of images is sourced from a video stream. The analyzingpixels can further include analyzing pixels within still images, wherethe still images can include visible light images, near-infrared images,and so on. The images can include video and/or still images that arecollected while the person is within the vehicle, stored images, and thelike. The flow 200 includes identifying a facial portion 230 of theperson. The identifying the facial portion can be based on theevaluation of pixels within the images for the presence of a facialportion within the video frames, still images, etc. When one or morepersons are found to be in a frame, then further analysis of the pixelsof the frame can be performed. If a person is not found in the frame,then a second video frame can be obtained and evaluated. A facialportion can include facial landmarks, facial regions, facialcharacteristics, and so on. The facial portion can include a full viewof a facial region or a partial view of a facial region. The identifyingthe facial portion within an image or frame can include scaling a face232 within the image. The scaling of the face can include magnificationor zooming in, reduction or zooming out, and so on. The identifying thefacial portion within the image or frame can further include orientingthe face 234. The orienting of the face can include rotation of the faceabout any axis (e.g. x, y, or z) or any combination of axes. The scalingand the orienting of the face can be performed to improve and enhanceanalysis of the face.

The flow 200 includes identifying one or more facial expressions 240based on the facial portion. The identifying one or more facialexpressions can be performed for more than one facial portion. Anadditional facial portion can include an additional facial portionanalyzed within an image resulting from a camera with a different viewof the person in the vehicle. The one or more facial expressions caninclude a smile, a frown, a smirk, a grimace, a yawn, and so on. Afacial expression can be based on one or both eyebrows raised. The flow200 includes classifying the one or more facial expressions 250 forcognitive response content. The classifying can be accomplished usingone or more classifiers, where the classifiers can be encoded in thesemiconductor chip, uploaded by a user, downloaded over a network, andso on. The classifying can be based on using classifier logic. Inembodiments, the classifier logic can be further trained to identifygender, age, ethnicity, or other demographic data for a face associatedwith the facial portion. The demographic data can be based on a yes orno determination, a range, a percentage, a threshold, etc. Inembodiments, the gender, age, or ethnicity can be provided with anassociated probability. The gender, age, ethnicity, and so on, can beself-provided by the person.

The flow 200 includes evaluating the cognitive response content 260 toproduce cognitive state information for the person. The producingcognitive state information can be based on a score, an assigned value,a percentage, a threshold, and the like. In embodiments, the cognitivestate information that was analyzed can be based on intermittentoccurrences of the facial portion within a series of images. Theintermittent occurrences of the facial portion can be based ontechniques used to capture the video or images such as capturing videobased on a time or a duration since a previous image was captured. Theintermittent occurrences can be based on capturing video when line ofsight is present between an imaging device and a person in the vehicle.The evaluating cognitive response content can be based on tracking logicwhich can identify that a face has left the images from the videostream. In further embodiments, an additional facial portion from animage of an additional person within the vehicle can be evaluated,identified, classified, and scored to produce additional cognitive stateinformation for the additional person.

The flow 200 includes updating a cognitive state profile 270 of anindividual associated with the facial portion. A cognitive state profileof an individual can include a variety of data associated with a person.In embodiments, the cognitive state profile summarizes the cognitivestate information of the individual. The cognitive state profile caninclude one or more cognitive states such as happy, sad, distracted,impaired, etc. In embodiments, the cognitive state profile can be basedon cognitive state event temporal signatures. A cognitive state eventtemporal signature can include an onset or intensity of the cognitivestates, decay of cognitive states, duration of the cognitive states, andso on. The cognitive state profile of an individual can be compared withother cognitive state profiles of the person. In a usage example, acognitive state profile of the person can be compared to the cognitivestate profile generated for the person during previous travel within thevehicle, travel within a second vehicle, and so on. The comparison ofcognitive state profiles can be used to identify changes in thecognitive states of the person such as drowsiness, impairment,distraction, and so on. The cognitive state profile for the person canbe compared to cognitive state profiles of other people. The cognitivestate profile can be updated based on changes in cognitive states,average cognitive states, etc. The flow 200 further includes augmentingthe cognitive state information based on audio data 272 collected fromwithin the vehicle. The audio data can be collected using a microphone,a transducer, or other audio capture device. In embodiments, the audiodata can include voice data. The voice data can include speech data,non-speech vocalizations, ambient sounds within or beyond the vehicle,etc. The audio data can be collected contemporaneously with the image.In a usage example, the audio data can augment the video data to detecta yawn while the person is covering her mouth with a hand. Suchaugmenting can be based on coordination between the video stream and anaudio stream. The cognitive state profile can further be updated basedon other data collected from the person in the vehicle. The flow 200includes tagging the cognitive state information 274 with sensor datareceived from the vehicle. The tagging can indicate that the personyawned while the temperature within the vehicle was too warm.

The flow 200 includes using a neural network 225 to accomplish thevarious elements illustrated in the flow 200, such as analyzing pixels220, identifying facial portions 230, identifying facial expressions240, classifying expressions 250, evaluating a cognitive response 260,and so on. Other elements within the flow 200 may be accomplished usingnon-neural network functionality, such as obtaining video 210, updatinga cognitive state profile 270, and manipulating a vehicle 280, althoughneural networks may be used in these cases as well.

The flow 200 includes manipulating the vehicle 280 based oncommunication of the cognitive state information to a component of thevehicle. Various aspects of the vehicle can be manipulated. Themanipulating the vehicle can include autonomous operation of the vehicleor semiautonomous operation of the vehicle. The manipulating the vehiclecan include changing operation of the vehicle from autonomous orsemiautonomous operation to manual operation of the vehicle, changingfrom manual operation of the vehicle to autonomous or semiautonomousoperation, initiating a vehicle shutdown, and so on. In embodiments, themanipulation of the vehicle can include a locking out operation;recommending a break for an occupant; recommending a different route forthe vehicle; recommending how far to drive; responding to traffic;adjusting seats, mirrors, climate control, lighting, music, audiostimuli, or interior temperature; brake activation; or steering control.The communication of the cognitive state information can includecommunication with the convolutional processing logic encoded within thesemiconductor chip, communication with an electronic device associatedwith the person within the vehicle, etc. The communication can beaccomplished using Wi-Fi™, LTE™, or another communication technique. Inembodiments, the cognitive state information can be used to communicateone or more of drowsiness, fatigue, distraction, sadness, stress,happiness, anger, frustration, confusion, disappointment, hesitation,cognitive overload, focusing, engagement, attention, boredom,exploration, confidence, trust, delight, disgust, skepticism, doubt,satisfaction, excitement, laughter, calmness, curiosity, humor,depression, envy, sympathy, embarrassment, poignancy, or mirth.

Various steps in the flow 200 may be changed in order, repeated,omitted, or the like without departing from the disclosed concepts.Various embodiments of the flow 200 can be included in a computerprogram product embodied in a non-transitory computer readable mediumthat includes code executable by one or more processors. Variousembodiments of the flow 200 can be included on a semiconductor chip andimplemented in special purpose logic, programmable logic, and so on.

FIG. 3 is a flow diagram for convolutional processing device usage.Convolutional processing, which can be accomplished using logic encodedwithin a device such as a semiconductor chip, can accomplish imageanalysis. The image analysis can enable facial evaluation in vehicles.In embodiments, images of one or more persons in a vehicle can beobtained. An image can be analyzed, where the analysis identifies afacial portion of the person. One or more facial expressions can beidentified based on the facial portion. The facial expressions can beclassified for cognitive response content, and the cognitive responsecontent can be scored to produce cognitive state information for theperson. The vehicle can be manipulated based on communication of thecognitive state information to a component of the vehicle. Inembodiments, the vehicle includes an autonomous vehicle or asemiautonomous vehicle. The component of the vehicle can includelighting, climate controls, entertainment controls, lock-out based onvehicle operator impairment, and so on.

The flow 300, or portions thereof, can be implemented by encoding logicin semiconductor chip. The logic that is encoded can include evaluationlogic, identification logic, and so on, where the various encoded logicscan accomplish convolutional processing for analysis such as imageanalysis. The flow 300 includes using an image analysis device 310 thatcontains image analysis logic encoded in a semiconductor chip. Thesemiconductor chip can be used to determine cognitive states byanalyzing captured videos of one or more people, as previouslydescribed. The videos can include a video feed or another video source,where the video feed can be made up of video frames that can be streamedfrom a camera. The video frames can be extracted from a video, and thevideo frames can include one or more people. The video frames can bebased on video that includes intermittent occurrences of a facialportion within a series of images. The camera can be any type of imagecapture device capable of capturing data to be used in an electronicsystem. The camera can include a webcam, a video camera, a still camera,a thermal imager, a CCD device, a phone camera, a three-dimensionalcamera, a light field camera, multiple cameras to obtain differentaspects or views of a person, or any other type of image capturetechnique.

The flow 300 includes training evaluation logic to analyze pixels withinan image 320 of a person in a vehicle. The analysis identifies a facialportion of the person. The evaluation logic that performs the analysisof pixels can be used to determine whether one or more facial portionsare present within a given video frame. When one or more facial portionsare found, then analysis can be performed on the video frame. When nofacial portions are located within the given frame, the next frame canbe obtained. A determination can be made as to whether analysis can beperformed for this next video frame. Recall that the absence of one ormore facial portions within a given frame can indicate that the one ormore facial portions present in a prior frame have exited the currentframe. In this case, identifiers determined for a prior frame can beretained in the event that one or more facial portions return in afuture frame. Discussed throughout, convolutional processing, which caninclude convolutional logic, can comprise one or more techniques thatcan be used for image analysis. In embodiments, the convolutionalprocessing logic comprises a deep neural network. The deep neuralnetwork can accomplish deep learning, where the deep learning can beapplied to image analysis. The flow 300 includes using a neural network325 for the analyzing pixels within the image. Discussed below, the useof a neural network such as a deep neural network, is applied to otherimage analysis techniques.

The identification of a facial portion within an image of a person in avehicle can be accomplished by analyzing pixels within the image, usinga sliding a window, using edge detection, or another technique. Thewindow can include a window of any size and shape appropriate to theidentifying the facial portion. The flow 300 further includes using theconvolutional processing device for tracking 322 the facial portion. Thetracking can be based on tracking logic. The tracking the facial portioncan include tracking between images, between video frames, and so on. Inembodiments, tracking logic can be trained for tracking the facialportion and identifying that the facial portion is no longer withinimages from the video stream. A facial portion might no longer be withinan image due to a person turning away, leaning down, exiting a vehicle,changing positions within the vehicle, and so on. In embodiments, thetracking logic can identify that a face has left the images from thevideo stream. In a usage example, a person can stop a vehicle and canexit the vehicle for various purposes such as shopping, eating, fuelingthe vehicle, and so on. The person can then return to the vehicle whenthe task has been completed. In embodiments, the tracking logic canidentify that the face has returned to the images from the video streamand can associate information previously collected about the face beforethe face left the video stream. As previously discussed, theconvolutional processing device can be used to track one or more facialportions of one or more faces, where the portions can includeidentifying eyes, eyebrows, ears, a nose, a mouth, a chin, and so on.The facial portions can include identifying characteristics,distinguishing marks, etc. The flow 300 further uses the convolutionalprocessing device to perform scaling of a facial feature 324. Scaling ofthe facial feature can include zooming in (magnifying), zooming out(shrinking), and other sizing and resizing techniques. The scaling canbe performed on the one or more facial portions that can be identifiedin the one or more images. The flow 300 further uses the convolutionalprocessing device to orient the face 326. Orienting the face can includerotation of the face about an axis (e.g. x, y, and z axes), tilting aface, and so on. In other embodiments, the device can further performimage correction for the image including one or more of lightingcorrection, contrast correction, near infrared lighting correction, ornoise filtering. The image correction can compensate for low lightconditions, changeable light conditions, partially obscured views fromone or more cameras to a person, etc. The image correction can be basedon a variety of signal and image processing techniques includinghigh-pass filtering, low-pass filtering, band-pass filtering,cross-correlation, etc.

The flow 300 includes identifying one or more facial expressions 330based on the facial portion. The device containing convolutionalprocessing logic can be used to perform various algorithms andheuristics to identify facial expressions. The facial expressions can bebased on determining facial landmarks, facial regions, facialcharacteristics, and so on. The determining facial expressions can bebased on determining one or more action units (AUs). The determiningfacial expressions can be based on detecting an onset, a duration, adecay, an intensity, and so on, associated with a facial expression. Thefacial expressions can include a neutral expression, a smile, a frown asmirk, and so on. The flow 300 includes classifying the one or morefacial expressions 340 for cognitive response content. The classifyingcan be based on using classifying logic that can be trained or encodedto perform one or more classifying techniques. The classifying can beaccomplished using one or more classifiers that can be included in thesemiconductor chip, code that can be operated on by the semiconductorchip, and the like. The classifiers can be uploaded by a user,downloaded from a networked or cloud-based repository, etc. Thecognitive state content can be based on which facial expressionsoccurred, the intensities and/or durations of the facial expressions,and the like. The classifying can be based on demographic information.In embodiments, the classifier logic can be further trained to identifya gender, age, ethnicity, or other demographic information, for a faceassociated with the facial portion. The identification of demographicinformation can be based on value, a threshold, a range of values, etc.In embodiments, the gender, age, or ethnicity can be provided with anassociated probability. In other embodiments, the device can send one ormore images to a web service for external classification based on thecognitive state information. The web service can include a subscriptionor contract service.

The flow 300 includes evaluating the cognitive response content 350. Theevaluating the cognitive response content can be accomplished using thedevice containing convolutional processing logic. In embodiments, thecognitive response content includes facial expressions. The cognitiveresponse content can further include an attitude or tilt of a head, adirection of gaze, and the like. The cognitive response content can beevaluated based on a value, a percentage, a range of values, and so on.The evaluating can be based on scoring, where a score can be assigned tocognitive response content. The cognitive response score can be based onthe alignment of responses to a baseline, including attunement to socialnorms, in some cases. Various parameters can be used as a basis fordetermining the cognitive response score. For example, the parameterscan include self-awareness, social skill, and empathy. The cognitiveresponse score can include an emotion, an intensity of the emotion, andso on. The cognitive response score can provide information, such asinformation on happiness, based on the regions of the face. The regionsof the face can include a mouth where the mouth is smiling, neutral,frowning, smirking, etc. Similarly, the cognitive response score canprovide information on other emotions including sadness, agitation,irritation, confusion, and so on. The cognitive response score canprovide information on concentration based on the regions of the faceincluding eyebrows, where the eyebrows are furrowed. The emotion scorecan provide information on other emotions including surprise, based onthe eyebrows being raised.

In the flow 300, the cognitive response content is evaluated to producecognitive state information 352 for the person. The cognitive stateinformation can be used to relate or communicate one or more cognitivestates. In embodiments, the cognitive state information can be used by asoftware application running on a processor coupled to the device. Theapplication can also be running on a server; on a remote processoravailable to the device; through a network such as a computer network, acloud, or mesh processor; etc. In embodiments, the cognitive stateinformation can be used to communicate one or more of drowsiness,fatigue, distraction, sadness, stress, happiness, anger, frustration,confusion, disappointment, hesitation, cognitive overload, focusing,engagement, attention, boredom, exploration, confidence, trust, delight,disgust, skepticism, doubt, satisfaction, excitement, laughter,calmness, curiosity, humor, depression, envy, sympathy, embarrassment,poignancy, or mirth. The cognitive state information can be augmentedusing further data collected in a vehicle. Embodiments further includelogic for augmenting the cognitive state information based on audio datacollected from within the vehicle, where the audio data is collectedcontemporaneously with the image. The cognitive state information canalso be augmented with physiological data. The physiological data can becollected using sensors, cameras, and so on. In embodiments,physiological information can be gleaned from a video containing theimage. The cognitive state information can be processed, analyzed,stored, and so on. In embodiments, the device can further performsmoothing of the cognitive state information.

The flow 300 includes manipulating the vehicle 360 based oncommunication of the cognitive state information to a component of thevehicle. The communication of the cognitive state information can beaccomplished using wireless techniques such as Wi-Fi™ Long TermEvolution (LTE™), and so on. The cognitive state information can becommunicated to an electronic device such as a smartphone associatedwith the person in the vehicle. The component of the vehicle can includeequipment position adjustments, vehicle climate control, vehicleentertainment center control, media suggestions, and so on. Thecomponent of the vehicle can be associated with other manipulationcontrols of the vehicle such as autonomous control, semiautonomouscontrol, etc. In embodiments, the manipulation of the vehicle caninclude a locking out operation; recommending a break for an occupant;recommending a different route for the vehicle; recommending how far todrive; responding to traffic; an adjusting of seats, mirrors, climatecontrol, lighting, music, audio stimuli, or interior temperature; brakeactivation, or steering control.

FIG. 4 illustrates image collection for cognitive state analysis. Imagecollection can be accomplished using a webcam, a video camera, a stillcamera, or another image capture device. The captured images can beanalyzed using a semiconductor processor for facial evaluation of one ormore persons within a vehicle. The semiconductor processor or chip caninclude a semiconductor chip within a device comprising one or moresemiconductor chips. The device can include a computing device such as alaptop computer, a mobile device 430 such as a smartphone, tablet, orPDA, and so on. The example 400 shows a person 410 viewing an event onone or more electronic displays such as electronic display 420. Inpractice, any number of displays can be shown to the person 410. Anevent can be a media presentation, where the media presentation can beviewed on an electronic display. The media presentation can include anadvertisement, a political campaign announcement, a TV show, a movie, avideo clip, a slide show, an educational program, or any other type ofmedia presentation. In the example 400, the person 410 has a line ofsight 412 to an electronic display 420. Similarly, the person 410 alsohas a line of sight 414 to the display of the mobile device 430. Whileone person has been shown, in practical use, embodiments of the presentinvention can analyze groups of people comprising tens, hundreds, orthousands of individuals or more. In embodiments including groups ofpeople, each person has a line of sight 412 to the event or mediapresentation rendered on the digital display 420, and/or each person hasa line of sight 414 to the event or media presentation rendered on adigital display of the mobile device 430. The plurality of capturedvideos can be of people who are viewing substantially identical mediapresentations or events, or conversely, the videos can capture peopleviewing different events or media presentations.

The display 420 can comprise a television monitor, a projector, acomputer monitor (including a laptop screen, a tablet screen, a net bookscreen, etc.), a projection apparatus, and the like. The portable devicedisplay 430 can include a cell phone display, a smartphone display, amobile device display, a PDA display, a tablet display, or anotherelectronic display. A camera can be used to capture images and video ofthe person 410. In the example 400, a camera 432 coupled to the mobiledevice 430 has a line of sight 434 to the person 410. Other cameras canbe used, including a webcam, a room camera, a wireless camera, etc. Inembodiments, a room camera 460 or web camera has a line of sight 462 tothe person 410. In a usage example, the webcam can be a networkeddigital camera that can take still and/or moving images of the face andpossibly the body of the person 410. The device camera 432 can be usedto capture one or more of the facial data and the physiological data.

The camera 432 coupled to the mobile device 430 can be used to capturedata from the person 410. In embodiments, the camera 432 or multiplecameras are used to capture data from a plurality of people. The camera432 can be built into the device or can be separate from but linked tothe device. The camera 432 can refer to any camera, including a cameraon a computer (such as a laptop, a netbook, a tablet, or the like), avideo camera, a still camera, a 3-D camera, a thermal imager, a CCDdevice, a three-dimensional camera, a light field camera, multiplewebcams used to show different views of the viewers, or any other typeof image capture apparatus that allows captured image data to be used inan electronic system. In addition, the camera 432 can refer to a cellphone camera as shown, a mobile device camera (including, but notlimited to, a front-side camera and a back-side camera), and so on. Thecamera 432 can capture a video or a plurality of videos of the person orpersons viewing the event or situation displayed on the electronicdisplay 420. The plurality of videos can be captured of people who areviewing substantially identical situations, such as viewing mediapresentations or events. The videos can be captured by a single camera,an array of cameras, randomly placed cameras, a mix of camera types, andso on. As mentioned above, media presentations can comprise anadvertisement, a political campaign announcement, a TV show, a movie, avideo clip, an educational program, or any other type of mediapresentation. The media can be oriented toward an emotion. For example,the media can include comedic material to evoke happiness, tragicmaterial to evoke sorrow, and so on.

A video capture module 440 can receive the facial data collected by thecamera 432, the camera 460, and so on. The facial data can be receivedusing a wired network, a wireless link 442, or other communicationtechnique. The wireless link can be based on Wi-Fi™, Bluetooth™, Zigbee™NFC™ and so on. The video data can include streamed video data, wherethe videos can be streamed from the camera 432. The videos can includevideo frames obtained by the camera 432. The video capture module 440can decompress the video into a raw format from a compressed format suchas H.264, MPEG-2, or the like. Facial data that is received can bereceived in the form of a plurality of videos, with the plurality ofvideos coming from a plurality of devices, cameras, etc. The pluralityof videos can be of one person and/or of a plurality of people who areviewing substantially identical situations or substantially differentsituations. The facial data can include information on action units,head gestures, eye movements, muscle movements, expressions, smiles, andthe like.

The raw video data can then be processed for expression analysis 450.The processing can include analysis of expression data, action units,gestures, cognitive states, and so on. Facial data as contained in theraw video data can include information on one or more of action units,head gestures, smiles, brow furrows, squints, lowered eyebrows, raisedeyebrows, attention, and the like. The action units can be used toidentify smiles, frowns, and other facial indicators of expressions.Gestures can also be identified, such as a head tilt to the side, aforward lean, a smile, a frown, as well as many other gestures. Othertypes of data including physiological data can be obtained, where thephysiological data can be obtained through the camera 432 withoutcontacting the person or persons. Respiration, heart rate, heart ratevariability, perspiration, temperature, and other physiologicalindicators of cognitive state can be determined by analyzing the imagesand the video data. All of this analysis can be implemented andperformed, or augmented by, semiconductor logic.

FIG. 5 is an example showing a second face and associated detection.Such detection and analysis can be performed by a device such as adevice based on semiconductor logic. A device such as an analysis devicecan be used to perform face detection for a second face within an image,as well as facial tracking between images. Analysis and detection of asecond face can enable facial evaluation in vehicles. A facial portionof a person can be identified by analyzing pixels within an image of aperson in a vehicle. One or more facial expressions can be identifiedbased on the facial portion, and the one or more facial expressions canbe classified for cognitive response content. The cognitive responsecontent can be scored to produce cognitive state information for theperson, and the vehicle can be manipulated based on communication of thecognitive state information to a component of the vehicle.

One or more videos, video clips, still images based on visible ornear-infrared light, and so on, can be captured using one or moreimaging devices. The imaging devices can be located within a vehicle,within a second vehicle, beyond the vehicle, and so on. The one or morecaptured videos can contain one or more faces. The video or videos thatcontain the one or more faces can be partitioned into a plurality offrames, and the frames can be analyzed for the detection of the one ormore faces. The analysis of the one or more video frames can be based onone or more classifiers. A classifier can be an algorithm, heuristic,function, or piece of code that can be used to identify into which of aset of categories a new or particular observation, sample, datum, etc.should be placed. The decision to place an observation into a categorycan be based on training the algorithm or piece of code, for example, byanalyzing a known set of data, known as a training set. The training setcan include data for which category memberships of the data can beknown. The training set can be used as part of a supervised trainingtechnique. If a training set is not available, then a clusteringtechnique can be used to group observations into categories. This latterapproach, or unsupervised learning, can be based on a measure (i.e.distance) of one or more inherent similarities among the data that isbeing categorized. When the new observation is received, then theclassifier can be used to categorize the new observation. Classifierscan be used for many analysis applications including analysis of one ormore faces. The use of classifiers can be the basis of analyzing the oneor more faces for gender, ethnicity, and age; for detection of one ormore faces in one or more videos; for detection of facial features; andso on. The observations can be analyzed based on one or more of a set ofquantifiable properties. The properties can be described as features andexplanatory variables and can include various data types that caninclude numerical (integer-valued, real-valued), ordinal, categorical,and so on. Some classifiers can be based on a comparison between anobservation and prior observations, as well as based on functions suchas a similarity function, a distance function, and so on.

Classification can be based on various types of algorithms, heuristics,codes, procedures, statistics, and so on. Many techniques for performingclassification exist. For example, classification of one or moreobservations into one or more groups can be based on distributions ofthe data values, probabilities, and so on. Classifiers can be binary,multiclass, linear, and so on. Algorithms for classification can beimplemented using a variety of techniques including neural networks,kernel estimation, support vector machines, use of quadratic surfaces,and so on. Classification can be used in many application areas such ascomputer vision, speech and handwriting recognition, and so on.Classification can be used for biometric identification of one or morepeople in one or more frames of one or more videos.

Returning to FIG. 5, the detection of the second face can includeidentifying facial landmarks, generating a bounding box, and predictinga bounding box and landmarks for a next frame, where the next frame canbe one of a plurality of frames of a video containing faces. A firstvideo frame 500 includes a frame boundary 510, a first face 512, and asecond face 514. The frame 500 also includes a bounding box 520. Faciallandmarks can be generated for the first face 512. Face detection can beperformed to initialize a second set of locations for a second set offacial landmarks for a second face within the video. Facial landmarks inthe video frame 500 can include the facial landmarks 522, 524, and 526.The facial landmarks can include corners of a mouth, corners of eyes,eyebrow corners, the tip of the nose, nostrils, chin, the tips of ears,and so on. The performing of face detection on the second face caninclude performing facial landmark detection with the first frame fromthe video for the second face and can include estimating a second roughbounding box for the second face based on the facial landmark detection.For example, the estimating of a second rough bounding box can includethe bounding box 520. Bounding boxes can also be estimated for one ormore other faces within the frame 510. The bounding box can be refined,as can one or more facial landmarks. The refining of the second set oflocations for the second set of facial landmarks can be based onlocalized information around the second set of facial landmarks. Thebounding box 520 and the facial landmarks 522, 524, and 526 can be usedto estimate future locations for the second set of locations for thesecond set of facial landmarks in a future video frame from the firstvideo frame.

A second video frame 502 is also shown. The second video frame 502includes a frame boundary 530, a first face 532, and a second face 534.The second frame 502 also includes a bounding box 540 and the faciallandmarks 542, 544, and 546. In other embodiments, any number of faciallandmarks are generated and used for facial tracking of the two or morefaces of a video frame, such as the shown second video frame 502. Facialpoints from the first face can be distinguished from other facialpoints. In embodiments, the other facial points include facial points ofone or more other faces. The facial points can correspond to the facialpoints of the second face. The distinguishing of the facial points ofthe first face and the facial points of the second face can be used todifferentiate between the first face and the second face, to trackeither or both of the first face and the second face, and so on. Otherfacial points can correspond to the second face. As mentioned above, anynumber of facial points can be determined within a frame. One or more ofthe other facial points that are determined can correspond to a thirdface. The location of the bounding box 540 can be estimated, where theestimating can be based on the location of the generated bounding box520 shown in the prior frame 500. The three facial points shown, facialpoints 542, 544, and 546, might lie within the bounding box 540 or mightnot lie partially or completely within the bounding box 540. Forexample, the second face 534 might have moved between the first videoframe 500 and the second video frame 502. Based on the accuracy of theestimating of the bounding box 540, a new estimation can be determinedfor a third, future frame from the video, and so on. The evaluation canbe performed, all or in part, on semiconductor-based logic.

FIG. 6 illustrates a semiconductor chip with classifiers. One or moreclassifiers can be used to analyze images that include facialinformation. The classifiers can be used to locate a facial landmark,region, or feature; to determine facial expressions; and so on.Classifiers for image analysis can use a semiconductor processor forfacial evaluation in vehicles. Pixels within an image of a person in avehicle are analyzed, where the analysis identifies a facial portion ofthe person. One or more facial expressions are identified based on thefacial portion. The one or more facial expressions are classified forcognitive response content. The cognitive response content is scored toproduce cognitive state information for the person. The vehicle ismanipulated based on communication of the cognitive state information toa component of the vehicle.

In the diagram 600, an application 610, hereafter referred to as an app,is shown loaded onto a device. The device can include any of a range ofdevices, such as portable devices including laptop computers andultra-mobile PCs; mobile devices such as smartphones, PDAs, and tablets;and wearable devices such as glasses and wrist watches, etc. Any numberof apps can be loaded or running on the device. The apps can include asocial networking app, such as Facebook™, Digg™, Google+™ Linkedln™,Tumblr™, Foursquare™, Yelp™, Waze™ and so on. Numerous other types ofapps can likewise utilize emotional enablement. Emotional enablement ofan app can allow a user to automatically express her or his emotionswhile using the app. In many cases, the devices contain built-incameras, but some devices might employ external cameras that areconnected to the device, accessible by the device, and so on. Thesemiconductor chip with classifiers can enable image analysis for facialevaluation in vehicles. The facial evaluation can include identifying afacial portion of a person in an image by analyzing pixels within theimage. Facial expressions can be identified based on the facial portion,and the facial expressions can be classified for cognitive responsecontent. The cognitive response content can be scored to producecognitive state information, and the vehicle can be manipulated based oncommunication of the cognitive state information to a component of thevehicle.

In the example shown, an app 610 communicates with a semiconductor chip620 which allows for emotionally enabling the app. In some embodiments,the semiconductor chip is a stand-alone chip, a custom chip, an FPGA, amodule included in a chip, and so on. The semiconductor chip 620 shownincludes multiple classifiers to process cognitive state data and infercognitive states. The classifiers can be employed to map the regionswithin a face for emotional content. The cognitive states can includeone or more of stress, sadness, anger, happiness, frustration,confusion, disappointment, hesitation, cognitive overload, focusing,engagement, attention, boredom, exploration, confidence, trust, delight,disgust, skepticism, doubt, satisfaction, excitement, laughter,calmness, and curiosity. One or more cognitive states can be analyzed todetermine emotional states, moods, and other useful information whichcan prove difficult for an individual to self-identify. In embodiments,one or more classifiers are present in a semiconductor chip. In thefigure shown, three example classifiers are present: classifier 1 622,classifier 2 624, and classifier N 626. While classifiers are typicallycode or data from a cloud or another remote source, classifiers can bestored locally on the semiconductor chip in some cases. In embodiments,any number of classifiers is possible. The classifiers can be obtainedfrom any of a variety of sources, including by Internet download, froman application vendor site, from user-developed code, and so on.Similarly, new classifiers can be obtained from a variety of sources.The classifiers in the semiconductor chip can be updated automatically.The classifiers can be used to identify deviations from a baselinefacial expression. The baseline facial expression can be a standardfacial expression, a typical facial expression for a person, and so on.

Various communication channels can exist between an app and thesemiconductor chip. For example, the app 610 can communicate with thesemiconductor chip 620 via a channel 612 and can receive a communicationback from the semiconductor chip via the same channel or anotherchannel, such as a second channel 614. The semiconductor chip 620 canreceive an initialization instruction or another communication throughthe channel 612 from the app 610. The semiconductor chip can performvarious operations based on the initialization. The operations performedcan include one of more of the classifiers 1 622 through N 626. Theoperations performed can include mapping the regions within the face foremotional content and evaluating the emotional content to produce anemotion score based on the face and the mapped regions. Information onthe one or more emotional states, on the mapping of the regions withinthe face for emotional content, and on the evaluating the emotionalcontent to produce an emotion score, etc. can be returned to the app 610using the second channel 614.

The semiconductor chip 620 can use classifiers to process and analyzecognitive state data gathered from a user or users. In embodiments, thedata is in the form of an image or video of the user or users. The imageor video can be obtained from a variety of sources, including one ormore cameras 630, video file storage systems 640, or cloud-basedresources 650, and can be obtained using a variety of networkingtechniques, including wired and wireless networking techniques. Inembodiments, the images are from a collection of photographs, an album,or another grouping of images or videos. The application can passparameters or information on the source of the video or images thatcontain cognitive state data to the semiconductor chip. Cognitive stateinformation, when analyzed from the cognitive state data, can aidindividuals in identifying emotional states and moods. In embodiments,the app 610, semiconductor chip 620, camera 630, and video file storagesystems 640 reside on the same device.

The classifiers 622, 624, and 626 can be utilized by support vectormachine analysis to identify the emotional content. The support vectormachine can be used for machine learning. The support vector machine caninclude supervised learning models and learning algorithms and can beused to analyze the emotional content for the classification. Thesupport vector machine can use a pre-trained algorithm. The algorithmcan be used to identify the emotional content. In some embodiments, thepre-trained algorithm serves as a starting point in the machine learningand can be modified to improve identification of the emotional content.The support vector machine can generate the emotion score. The emotionscore can be used by a software application running on a processorcoupled to the device or semiconductor chip 620. In embodiments, theemotion score can be used directly by accessing special hardwareincluded in the semiconductor chip 620. In some embodiments, theclassifiers on the semiconductor chip can be lighter or simpler versionsthat can assist in sifting image data. Then a fuller set of classifierscan be performed on web-based servers, if warranted.

FIG. 7 shows apps calling the semiconductor chip analysis machine.Programs, processes, routines, applications, apps, and so on, can beprocessed on a semiconductor chip. In the example 700, one or more apps710 call a semiconductor chip 720. The apps can include image processingapps which use the semiconductor chip for facial evaluation in vehicles.Pixels within an image of a person in a vehicle can be analyzed toidentify a facial portion of the person. Facial expressions can beidentified based on the facial portion, and the facial expressions canbe classified for cognitive response content. The cognitive responsecontent can be scored to produce cognitive state information, and thevehicle can be manipulated based on communication of the cognitive stateinformation to a component of the vehicle. The apps such as the imageanalysis apps can reside on a device, where the device can be a portabledevice such as a laptop or ultra-mobile PC; a mobile device such as asmartphone, tablet, or personal digital assistant (PDA); a wearabledevice such as glasses or a watch; and so on. In embodiments, the apps710 and the semiconductor chip 720 reside on the same device. The apps710 can include a single app, such as an app 1 712. In some embodiments,the apps 710 comprise a plurality of applications, such as an app 1 712,an app 2 714, an app 3 716, an app N 718, and so on. The apps cancomprise any of a variety of apps, including social media apps. Thesemiconductor chip 720 can provide emotional enablement to a device onwhich the semiconductor chip 720 resides. A user can choose toemotionally enable any number of apps loaded on her or his device. Theone or more apps 710 can send video, images, raw data, or other userinformation to the semiconductor chip 720 for analysis. The images,video, user information, and the like can be generated by the device,obtained by the device, loaded onto the device, and so on.

The semiconductor chip 720 can include analysis capabilities in the formof an analysis machine 730. In some embodiments, the semiconductor chip720 also communicates with other devices and services, including a webservice. Analysis of raw data can be performed on the device, on the webservice, or on both the device and the service. The raw data can includeimages, video, video clips, user information, and so on. In at least oneembodiment, all of the analysis needed by the one or more apps 710 isperformed on the device. The analysis machine 730 can analyze the imageor video to determine one or more cognitive states, where the cognitivestates can include one or more of stress, sadness, happiness, anger,frustration, confusion, disappointment, hesitation, cognitive overload,focusing, engagement, attention, boredom, exploration, confidence,trust, delight, disgust, skepticism, doubt, satisfaction, excitement,laughter, calmness, and curiosity. The analysis machine 730 candetermine one or more emotional states based on the cognitive stateinformation. The analysis machine 730 can employ classifiers to map theregions within a face for emotional content. The classifiers can includefacial expressions such as happy, sad, angry, fearful, etc., as well asinformation such as race, gender, and so on. The classifiers can mapfacial regions including the mouth, eyes, eyebrows, etc. The analysismachine can evaluate emotional content to produce an emotion score basedon the face. The emotion score can be used by a software applicationrunning on a processor coupled to the device or semiconductor chip 720.In another embodiment, a hardware module coupled to the semiconductorchip 720, or incorporated into the chip, can use the emotion score. Theemotion score can be used to rank the intensity of the facialexpressions, for example.

FIG. 8 shows an example of live streaming of social video and audio. Thestreaming of social video and social audio can be applied to imageanalysis based on convolutional processing techniques encoded in asemiconductor chip. The image analysis enables facial evaluation invehicles. The live streaming can include cognitive state data, imagingdata, facial data, upper torso data, speech data, audio data, physiodata, etc. Images that include facial data for cognitive state analysiscan be obtained based on a variety of image capture techniques such asin-vehicle imaging devices, cameras beyond a vehicle that have a view ofthe vehicle, and so on. An image of a person in a vehicle is analyzed toidentify a facial portion of the person, such as facial regions, faciallandmarks, and so on. One or more facial expressions are identifiedbased on the facial portion, and the one or more facial expressions areclassified for cognitive response content. The cognitive responsecontent is scored to produce cognitive state information, and thevehicle is manipulated based on communication of the cognitive stateinformation to a component of the vehicle.

The live streaming and image analysis 800 can be facilitated by a videocapture device, a local server, a remote server, semiconductor-basedlogic, and so on. The streaming can be live streaming and can includecognitive state analysis, cognitive state event signature analysis, etc.Live streaming video is an example of one-to-many social media, wherevideo can be sent over a computer network such as the Internet from oneperson to a plurality of people using a social media app and/orplatform. Live streaming is one of numerous popular techniques used bypeople who want to disseminate ideas, send information, provideentertainment, and share experiences, and so on. Some of the livestreams, such as webcasts, online classes, sporting events, news,computer gaming, or video conferences can be scheduled, while others canbe impromptu streams that are broadcast as needed or when desired.Examples of impromptu live stream videos can range from individualssimply wanting to share experiences with their social media followers,to live coverage of breaking news, emergencies, or natural disasters.The latter coverage is known as mobile journalism, or “mojo”, and isbecoming increasingly common. With this type of coverage, news reporterscan use networked, portable electronic devices to provide mobilejournalism content to a plurality of social media followers. Suchreporters can be quickly and inexpensively deployed as the need ordesire arises.

Several live streaming social media apps and platforms can be used fortransmitting video. One such video social media app is Meerkat™ whichcan link with a user's Twitter™ account. Meerkat™ enables a user tostream video using a handheld, networked electronic device coupled tovideo capabilities. Viewers of the live stream can comment on the streamusing tweets that can be seen and responded to by the broadcaster.Another popular app is Periscope™ which can transmit a live recordingfrom one user to his or her Periscope™ account and to other followers.The Periscope™ app can be executed on a mobile device. The user'sPeriscope™ followers can receive an alert whenever that user begins avideo transmission. Another live-stream video platform is Twitch™ whichcan be used for video streaming of video gaming and broadcasts ofvarious competitions and events.

The example 800 shows a user 810 broadcasting a video live stream and anaudio live stream to one or more people as shown by a first person 850,a second person 860, and a third person 870. A portable,network-enabled, electronic device 820 can be coupled to a front-sidecamera 822. The portable electronic device 820 can be a smartphone, aPDA, a tablet, a laptop computer, and so on. The camera 822 coupled tothe device 820 can have a line-of-sight view 824 to the user 810 and cancapture video of the user 810. The portable electronic device 820 can becoupled to a microphone (not shown). The microphone can capture voicedata 828 such as speech and non-speech vocalizations. In embodiments,non-speech vocalizations can include grunts, yelps, squeals, snoring,sighs, laughter, filled pauses, unfilled pauses, yawns, or the like. Thecaptured video and audio can be sent to an analysis or recommendationmachine 840 using a network link 826 to the network 830. The networklink can be a wireless link, a wired link, and so on. The analysismachine 840 can recommend to the user 810 an app and/or platform thatcan be supported by the server and can be used to provide a video livestream, an audio live stream, or both a video live stream and an audiolive stream to one or more followers of the user 810.

In the example 800, the user 810 has four followers: a first person 850,a second person 860, a third person 870, and a fourth person 880. Eachfollower has a line-of-sight view to a video screen on a portable,networked electronic device. In other embodiments, one or more followersfollow the user 810 using any other networked electronic device,including a computer. In the example 800, a first person 850 has aline-of-sight view 852 to the video screen of a device 854; a secondperson 860 has a line-of-sight view 862 to the video screen of a device864, a third person 870 has a line-of-sight view 872 to the video screenof a device 874, and a fourth person 880 has a line-of-sight view 882 tothe video screen of a device 884. The device 874 can also capture audiodata 878 from the third person 870, and the device 884 can furthercapture audio data 888 from the fourth person 880. The portableelectronic devices 854, 864, 874, and 884 can each be a smartphone, aPDA, a tablet, and so on. Each portable device can receive the videostream and the audio stream being broadcast by the user 810 through thenetwork 830 using the app and/or platform that can be recommended by theanalysis or recommendation machine 840. The network can include theInternet, a computer network, a cellular network, and the like. Thedevice 854 can receive a video stream and an audio stream using thenetwork link 856, the device 864 can receive a video stream and an audiostream using the network link 866, the device 874 can receive a videostream and an audio stream using the network link 876, the device 884can receive a video stream and an audio stream using the network link886, and so on. The network link can be a wireless link, a wired link, ahybrid link, and the like. Depending on the app and/or platform that canbe recommended by the analysis machine 840, one or more followers, suchas the followers shown 850, 860, 870, and 880, can reply to, comment on,or otherwise provide feedback to the user 810 using their respectivedevices 854, 864, 874, and 884.

The human face provides a powerful communications medium through itsability to exhibit numerous expressions that can be captured andanalyzed for a variety of purposes. In some cases, media producers areacutely interested in evaluating the effectiveness of message deliveryby video media. Such video media includes advertisements, politicalmessages, educational materials, television programs, movies, governmentservice announcements, etc. Automated facial analysis can be performedon one or more video frames containing a face in order to detect facialaction. Based on the facial action detected, a variety of parameters canbe determined, including affect valence, spontaneous reactions, facialaction units, and so on. The parameters that are determined can be usedto infer or predict emotional, mental, and cognitive states. Forexample, determined valence can be used to describe the emotionalreaction of a viewer to a video media presentation or another type ofpresentation. Positive valence provides evidence that a viewer isexperiencing a favorable emotional response to the video mediapresentation, while negative valence provides evidence that a viewer isexperiencing an unfavorable emotional response to the video mediapresentation. Other facial data analysis can include the determinationof discrete emotional states of the viewer or viewers.

Facial data can be collected from a plurality of people using any of avariety of cameras. A camera can include a webcam, a video camera, astill camera, a thermal imager, a CCD device, a phone camera, athree-dimensional camera, a depth camera, a light field camera, multiplewebcams used to show different views of a person, or any other type ofimage capture apparatus that can allow captured data to be used in anelectronic system. In some embodiments, the person is permitted to“opt-in” to the facial data collection. For example, the person canagree to the capture of facial data using a personal device such as amobile device or another electronic device by selecting an opt-inchoice. Opting-in can then turn on the person's webcam-enabled deviceand can begin the capture of the person's facial data via a video feedfrom the webcam or other camera. The video data that is collected caninclude one or more persons experiencing an event. The one or morepersons can be sharing a personal electronic device or can each be usingone or more devices for video capture. The videos that are collected canbe collected using a web-based framework. The web-based framework can beused to display the video media presentation or event as well as tocollect videos from multiple viewers who are online. That is, thecollection of videos can be crowdsourced from those viewers who electedto opt-in to the video data collection.

The videos captured from the various viewers who chose to opt in can besubstantially different in terms of video quality, frame rate, etc. As aresult, the facial video data can be scaled, rotated, and otherwiseadjusted to improve consistency. Human factors further contribute to thecapture of the facial video data. The facial data that is captured mightor might not be relevant to the video media presentation beingdisplayed. For example, the viewer might not be paying attention, mightbe fidgeting, might be distracted by an object or event near the viewer,or might be otherwise inattentive to the video media presentation. Thebehavior exhibited by the viewer can prove challenging to analyze due toviewer actions including eating, speaking to another person or persons,speaking on the phone, etc. The videos collected from the viewers mightalso include other artifacts that pose challenges during the analysis ofthe video data. The artifacts can include items such as eyeglasses(because of reflections), eye patches, jewelry, and clothing thatocclude or obscure the viewer's face. Similarly, a viewer's hair or haircovering can present artifacts by obscuring the viewer's eyes and/orface.

The captured facial data can be analyzed using the facial action codingsystem (FACS). The FACS seeks to define groups or taxonomies of facialmovements of the human face. The FACS encodes movements of individualmuscles of the face, where the muscle movements often include slight,instantaneous changes in facial appearance. The FACS encoding iscommonly performed by trained observers, but can also be performed onautomated, computer-based systems. Analysis of the FACS encoding can beused to determine emotions of the persons whose facial data is capturedin the videos. The FACS is used to encode a wide range of facialexpressions that are anatomically possible for the human face. The FACSencodings include action units (AUs) and related temporal segments thatare based on the captured facial expression. The AUs are open to higherorder interpretation and decision-making. These AUs can be used torecognize emotions experienced by the person who is being observed.Emotion-related facial actions can be identified using the emotionalfacial action coding system (EMFACS) and the facial action coding systemaffect interpretation dictionary (FACSAID). For a given emotion,specific action units can be related to the emotion. For example, theemotion of anger can be related to AUs 4, 5, 7, and 23, while happinesscan be related to AUs 6 and 12. Other mappings of emotions to AUs havealso been previously associated. The coding of the AUs can include anintensity scoring that ranges from A (trace) to E (maximum). The AUs canbe used for analyzing images to identify patterns indicative of aparticular cognitive and/or emotional state. The AUs range in numberfrom 0 (neutral face) to 98 (fast up-down look). The AUs includeso-called main codes (inner brow raiser, lid tightener, etc.), headmovement codes (head turn left, head up, etc.), eye movement codes (eyesturned left, eyes up, etc.), visibility codes (eyes not visible, entireface not visible, etc.), and gross behavior codes (sniff, swallow,etc.). Emotion scoring can be included where intensity is evaluated, andspecific emotions, moods, mental states, or cognitive states can beidentified.

The coding of faces identified in videos captured of people observing anevent can be automated. The automated systems can detect facial AUs ordiscrete emotional states. The emotional states can include amusement,fear, anger, disgust, surprise, and sadness. The automated systems canbe based on a probability estimate from one or more classifiers, wherethe probabilities can correlate with an intensity of an AU or anexpression. The classifiers can be used to identify into which of a setof categories a given observation can be placed. In some cases, theclassifiers can be used to determine a probability that a given AU orexpression is present in a given frame of a video. The classifiers canbe used as part of a supervised machine learning technique, where themachine learning technique can be trained using “known good” data. Oncetrained, the machine learning technique can proceed to classify new datathat is captured.

The supervised machine learning models can be based on support vectormachines (SVMs). An SVM can have an associated learning model that isused for data analysis and pattern analysis. For example, an SVM can beused to classify data that can be obtained from collected videos ofpeople experiencing a media presentation. An SVM can be trained using“known good” data that is labeled as belonging to one of two categories(e.g., smile and no-smile). The SVM can build a model that assigns newdata into one of the two categories. The SVM can construct one or morehyperplanes that can be used for classification. The hyperplane that hasthe largest distance from the nearest training point can be determinedto have the best separation. The largest separation can improve theclassification technique by increasing the probability that a given datapoint can be properly classified.

In another example, a histogram of oriented gradients (HoG) can becomputed. The HoG can include feature descriptors and can be computedfor one or more facial regions of interest. The regions of interest ofthe face can be located using facial landmark points, where the faciallandmark points can include outer edges of nostrils, outer edges of themouth, outer edges of eyes, etc. A HoG for a given region of interestcan count occurrences of gradient orientation within a given section ofa frame from a video, for example. The gradients can be intensitygradients and can be used to describe an appearance and a shape of alocal object. The HoG descriptors can be determined by dividing an imageinto small, connected regions, also called cells. A histogram ofgradient directions or edge orientations can be computed for pixels inthe cell. Histograms can be contrast-normalized based on intensityacross a portion of the image or the entire image, thus reducing anyinfluence from differences in illumination or shadowing changes betweenand among video frames. The HoG can be computed on the image or on anadjusted version of the image, where the adjustment of the image caninclude scaling, rotation, etc. The image can be adjusted by flippingthe image around a vertical line through the middle of a face in theimage. The symmetry plane of the image can be determined from thetracker points and landmarks of the image.

In embodiments, an automated facial analysis system identifies fivefacial actions or action combinations in order to detect spontaneousfacial expressions for media research purposes. Based on the facialexpressions that are detected, a determination can be made with regardto the effectiveness of a given video media presentation, for example.The system can detect the presence of the AUs or the combination of AUsin videos collected from a plurality of people. The facial analysistechnique can be trained using a web-based framework to crowdsourcevideos of people as they watch online video content. The video can bestreamed at a fixed frame rate to a server. Human labelers can code forthe presence or absence of facial actions including a symmetric smile,unilateral smile, asymmetric smile, and so on. The trained system canthen be used to automatically code the facial data collected from aplurality of viewers experiencing video presentations (e.g., televisionprograms).

Spontaneous asymmetric smiles can be detected in order to understandviewer experiences. Related literature indicates that as many asymmetricsmiles occur on the right hemi face as do on the left hemi face, forspontaneous expressions. Detection can be treated as a binaryclassification problem, where images that contain a right asymmetricexpression are used as positive (target class) samples and all otherimages as negative (non-target class) samples. Classifiers perform theclassification, including classifiers such as support vector machines(SVM) and random forests. Random forests can include ensemble-learningmethods that use multiple learning algorithms to obtain betterpredictive performance. Frame-by-frame detection can be performed torecognize the presence of an asymmetric expression in each frame of avideo. Facial points can be detected, including the top of the mouth andthe two outer eye corners. The face can be extracted, cropped, andwarped into a pixel image of a specific dimension (e.g. 96×96 pixels).In embodiments, the inter-ocular distance and vertical scale in thepixel image are fixed. Feature extraction can be performed usingcomputer vision software such as OpenCV™. Feature extraction can bebased on the use of HoGs. HoGs can include feature descriptors and canbe used to count occurrences of gradient orientation in localizedportions or regions of the image. Other techniques for countingoccurrences of gradient orientation can be used, including edgeorientation histograms, scale-invariant feature transformationdescriptors, etc. The AU recognition tasks can also be performed usingLocal Binary Patterns (LBPs) and Local Gabor Binary Patterns (LGBPs).The HoG descriptor represents the face as a distribution of intensitygradients and edge directions and is robust in its ability to translateand scale. Differing patterns, including groupings of cells of varioussizes and arranged in variously sized cell blocks, can be used. Forexample, 4×4 cell blocks of 8×8-pixel cells with an overlap of half ofthe block can be used. Histograms of channels can be used, includingnine channels or bins evenly spread over 0-180 degrees. In this example,the HoG descriptor on a 96×96 image is 25 blocks×16 cells×9 bins=3600,the latter quantity representing the dimension. AU occurrences can berendered. The videos can be grouped into demographic datasets based onnationality and/or other demographic parameters for further detailedanalysis. This grouping and other analyses can be facilitated viasemiconductor-based logic.

FIG. 9 shows example facial data collection including landmarks 900. Theanalysis of landmarks can be accomplished using logic such asconvolutional processing logic encoded in a semiconductor chip. Theconvolutional processing logic can accomplish image analysis for facialevaluation in vehicles. Pixels within an image of a person in a vehicleare analyzed, where the analysis identifies a facial portion of theperson. One or more facial expressions are identified based on thefacial portion. The one or more facial expressions are classified forcognitive response content, and the cognitive response content is scoredto produce cognitive state information for the person. The vehicle ismanipulated based on communication of the cognitive state information toa component of the vehicle.

A face 910 can be observed using a camera 930 in order to collect facialdata that includes facial landmarks. The facial data can be collectedfrom a plurality of people using one or more of a variety of cameras. Asdiscussed above, the camera or cameras can include a webcam, where awebcam can include a video camera, a still camera, a thermal imager, aCCD device, a phone camera, a three-dimensional camera, a depth camera,a light field camera, multiple webcams used to show different views of aperson, or any other type of image capture apparatus that can allowcaptured data to be used in an electronic system. The quality andusefulness of the facial data that is captured can depend, for example,on the position of the camera 930 relative to the face 910, the numberof cameras used, the illumination of the face, etc. For example, if theface 910 is poorly lit or over-exposed (e.g. in an area of brightlight), the processing of the facial data to identify facial landmarksmight be rendered more difficult. In another example, the camera 930being positioned to the side of the person might prevent capture of thefull face. Other artifacts can degrade the capture of facial data. Forexample, the person's hair, prosthetic devices (e.g. glasses, an eyepatch, and eye coverings), jewelry, and clothing can partially orcompletely occlude or obscure the person's face. Data relating tovarious facial landmarks can include a variety of facial features. Thefacial features can comprise an eyebrow 920, an outer eye edge 922, anose 924, a corner of a mouth 926, and so on. Any number of faciallandmarks can be identified from the facial data that is captured. Thefacial landmarks that are identified can be analyzed to identify facialaction units. For example, the action units that can be identified caninclude AU02 outer brow raiser, AU14 dimpler, AU17 chin raiser, and soon. Any number of action units can be identified. The action units canbe used alone and/or in combination to infer one or more cognitivestates and emotions. A similar process can be applied to gestureanalysis (e.g. hand gestures) with all of the analysis beingaccomplished or augmented by semiconductor-based logic.

FIG. 10 is a flow diagram for detecting facial expressions. This flow,or portions thereof, can be implemented in semiconductor logic. The flow1000 can be used to automatically detect a wide range of facialexpressions. A facial expression can produce strong cognitive signalsthat can indicate valence and discrete cognitive, mental, or emotionalstates. The discrete cognitive, mental, or emotional states can includecontempt, doubt, defiance, happiness, fear, anxiety, and so on. Thedetection of facial expressions can be based on the location of faciallandmarks or regions. The detection of facial expressions can be basedon analyzing pixels within an image of a person in a vehicle, where theanalysis identifies a facial portion of the person. The facialexpressions can be identified based on the facial portion, and thefacial expressions can be classified for cognitive response content. Thecognitive response content can be scored to produce cognitive stateinformation for the person, and a vehicle can be manipulated vehiclebased on communication of the cognitive state information to a componentof the vehicle. The detection of facial expressions can be based ondetermination of action units (AUs) where the action units aredetermined using FACS coding. The AUs can be used singly or incombination to identify facial expressions. Based on the faciallandmarks, one or more AUs can be identified by number and intensity.For example, AU12 can be used to code a lip corner puller and can beused to infer a smirk.

The flow 1000 begins by obtaining training image samples 1010. The imagesamples can include a plurality of images of one or more people. Humancoders who are trained to correctly identify AU codes based on the FACScan code the images. The training or “known good” images can be used asa basis for training a machine learning technique. Once trained, themachine learning technique can be used to identify AUs in other imagesthat can be collected using a camera, such as the camera 930 from FIG.9, for example. The flow 1000 continues with receiving an image 1020.The image can be received from the camera 930. As discussed above, thecamera or cameras can include a webcam, where a webcam can include avideo camera, a still camera, a thermal imager, a CCD device, a phonecamera, a three-dimensional camera, a depth camera, a light fieldcamera, multiple webcams used to show different views of a person, orany other type of image capture apparatus that can allow captured datato be used in an electronic system. The image that is received can bemanipulated in order to improve the processing of the image. Forexample, the image can be cropped, scaled, stretched, rotated, flipped,etc. in order to obtain a resulting image that can be analyzed moreefficiently. Multiple versions of the same image can be analyzed. Forexample, the manipulated image and a flipped or mirrored version of themanipulated image can be analyzed alone and/or in combination to improveanalysis. The flow 1000 continues with generating histograms 1030 forthe training images and the one or more versions of the received image.The histograms can be generated for one or more versions of themanipulated received image. The histograms can be based on a HoG oranother histogram. As described above, the HoG can include featuredescriptors and can be computed for one or more regions of interest inthe training images and the one or more received images. The regions ofinterest in the images can be located using facial landmark points,where the facial landmark points can include outer edges of nostrils,outer edges of the mouth, outer edges of eyes, etc. A HoG for a givenregion of interest can count occurrences of gradient orientation withina given section of a frame from a video, for example.

The flow 1000 continues with applying classifiers 1040 to thehistograms. The classifiers can be used to estimate probabilities, wherethe probabilities can correlate with an intensity of an AU or anexpression. In some embodiments, the choice of classifiers used is basedon the training of a supervised learning technique to identify facialexpressions. The classifiers can be used to identify into which of a setof categories a given observation can be placed. For example, theclassifiers can be used to determine a probability that a given AU orexpression is present in a given image or frame of a video. In variousembodiments, the one or more AUs that are present include AU01 innerbrow raiser, AU12 lip corner puller, AU38 nostril dilator, and so on. Inpractice, the presence or absence of any number of AUs can bedetermined. The flow 1000 continues with computing a frame score 1050.The score computed for an image, where the image can be a frame from avideo, can be used to determine the presence of a facial expression inthe image or video frame. The score can be based on one or more versionsof the image 1020 or manipulated image. For example, the score can bebased on a comparison of the manipulated image to a flipped or mirroredversion of the manipulated image. The score can be used to predict alikelihood that one or more facial expressions are present in the image.The likelihood can be based on computing a difference between theoutputs of a classifier used on the manipulated image and on the flippedor mirrored image, for example. The classifier that is used can be usedto identify symmetrical facial expressions (e.g. smile), asymmetricalfacial expressions (e.g. outer brow raiser), and so on.

The flow 1000 continues with plotting results 1060. The results that areplotted can include one or more scores for one or more frames computedover a given time t. For example, the plotted results can includeclassifier probability results from analysis of HoGs for a sequence ofimages and video frames. The plotted results can be matched with atemplate 1062. The template can be temporal and can be represented by acentered box function or another function. A best fit with one or moretemplates can be found by computing a minimum error. Other best-fittechniques can include polynomial curve fitting, geometric curvefitting, and so on. The flow 1000 continues with applying a label 1070.The label can be used to indicate that a particular facial expressionhas been detected in the one or more images or video frames whichconstitute the image that was received 1020. For example, the label canbe used to indicate that any of a range of facial expressions has beendetected, including a smile, an asymmetric smile, a frown, and so on.Various steps in the flow 1000 may be changed in order, repeated,omitted, or the like without departing from the disclosed concepts.Various embodiments of the flow 1000 can be included in a computerprogram product embodied in a non-transitory computer readable mediumthat includes code executable by one or more processors. Variousembodiments of the flow 1000, or portions thereof, can be included on asemiconductor chip and implemented in special purpose logic,programmable logic, and so on

FIG. 11 is a flow diagram for the large-scale clustering of facialevents. The clustering and evaluation of facial events can be augmentedusing semiconductor-based logic. As discussed above, collection andevaluation of data such as facial video data from one or more people caninclude a web-based framework. The web-based framework can be used tocollect facial video data from large numbers of people located over awide geographic area, or one or more people in a vehicle. The web-basedframework can include an opt-in feature that allows people to agree tofacial data collection. The web-based framework can be used to renderand display data to one or more people and can collect data from the oneor more people. In a usage example, the facial data collection can bebased on showing one or more viewers a video media presentation througha website. The web-based framework can be used to display the videomedia presentation or event and to collect videos from any number ofviewers who are online. That is, the collection of videos can becrowdsourced from those viewers who elected to opt in to the video datacollection. The video event can be a commercial, a political ad, aneducational segment, and so on. In embodiments, the large-scaleclustering of facial events can be based on image analysis using asemiconductor processor. The image analysis, which can includeconvolutional processing, can be used for facial evaluation in vehicles.

The flow 1100 begins with obtaining videos containing faces 1110. Thevideos can be obtained using one or more cameras, where the cameras caninclude a webcam coupled to one or more devices employed by the one ormore people using the web-based framework. The flow 1100 continues withextracting features from the individual responses 1120. The individualresponses can include videos containing faces observed by the one ormore webcams. The features that are extracted can include facialfeatures such as an eyebrow, a nostril, an eye edge, a mouth edge, andso on. The feature extraction can be based on facial coding classifiers,where the facial coding classifiers output a probability that aspecified facial action has been detected in a given video frame. Theflow 1100 continues with performing unsupervised clustering of features1130. The unsupervised clustering can be based on an event. Theunsupervised clustering can be based on a K-Means, where the K of theK-Means can be computed using a Bayesian Information Criterion (BICk),for example, to determine the smallest value of K that meets systemrequirements. Any other criterion for K can be used. The K-Meansclustering technique can be used to group one or more events intovarious respective categories.

The flow 1100 continues with characterizing cluster profiles 1140. Theprofiles can include a variety of facial expressions such as smiles,asymmetric smiles, eyebrow raisers, eyebrow lowerers, etc. The profilescan be related to a given event. For example, a humorous video can bedisplayed in the web-based framework and the video data of people whohave opted in can be collected. The characterization of the collectedand analyzed video can depend in part on the number of smiles thatoccurred at various points throughout the humorous video. Similarly, thecharacterization can be performed on collected and analyzed videos ofpeople viewing a news presentation. The characterized cluster profilescan be further analyzed based on demographic data. For example, thenumber of smiles resulting from people viewing a humorous video can becompared to various demographic groups, where the groups can be formedbased on geographic location, age, ethnicity, gender, and so on.

FIG. 12 shows example unsupervised clustering of features andcharacterization of cluster profiles. The unsupervised clustering andthe characterization of cluster profiles can be based on image analysis,where the image analysis uses a semiconductor processor for facialevaluation in vehicles. Features including samples of data such asfacial data, audio data, physiological data, and so on, can be clusteredusing unsupervised clustering. Various clusters can be formed, where theclusters include similar groupings of data observations such as facialdata observations. The example 1200 shows three clusters 1210, 1212, and1214. The clusters can be based on video collected from people who haveopted in to video collection. When the collected data is captured usinga web-based framework, the data collection can be performed on a grandscale, including hundreds, thousands, or even more participants who canbe located locally and/or across a wide geographic area. Unsupervisedclustering is a technique that can be used to process the large amountsof captured facial data and to identify groupings of similarobservations. The unsupervised clustering can also be used tocharacterize the groups of similar observations. The characterizationscan include identifying behaviors of the participants. Thecharacterizations can be based on identifying facial expressions andfacial action units of the participants. Some behaviors and facialexpressions can include faster or slower onsets, faster or sloweroffsets, longer or shorter durations, etc. The onsets, offsets, anddurations can all correlate to time. The data clustering that resultsfrom the unsupervised clustering can support data labeling. The labelingcan include FACS coding. The clusters can be partially or totally basedon a facial expression resulting from participants viewing a videopresentation, where the video presentation can be an advertisement, apolitical message, educational material, a public service announcement,and so on. The clusters can be correlated with demographic information,where the demographic information can include educational level,geographic location, age, gender, income level, and so on.

The cluster profiles 1202 can be generated based on the clusters thatcan be formed from unsupervised clustering, with time shown on thex-axis and intensity or frequency shown on the y-axis. The clusterprofiles can be based on captured facial data including facialexpressions, for example. The cluster profile 1220 can be based on thecluster 1210, the cluster profile 1222 can be based on the cluster 1212,and the cluster profile 1224 can be based on the cluster 1214. Thecluster profiles 1220, 1222, and 1224 can be based on smiles, smirks,frowns, or any other facial expression. The emotional states of thepeople who have opted-in to video collection can be inferred byanalyzing the clustered facial expression data. The cluster profiles canbe plotted with respect to time and can show a rate of onset, aduration, and an offset (rate of decay). Other time-related factors canbe included in the cluster profiles. The cluster profiles can becorrelated with demographic information, as described above.

FIG. 13A shows example tags embedded in a webpage. Once a tag isdetected, semiconductor-based logic can be used for convolutionalprocessing for facial evaluation in vehicles. The facial evaluation caninclude evaluating facial expressions. The facial expressions can beevaluated by analyzing pixels within an image of a person in a vehicle.The facial expressions can be classified for cognitive response content,and the cognitive response content can be scored. A vehicle can bemanipulated based on communication of the cognitive state information toa component of the vehicle. A webpage 1300 can include a page body 1310,a page banner 1312, and so on. The page body can include one or moreobjects, where the objects can include text, images, videos, audio, andso on. The example page body 1310 shown includes a first image, image 11320; a second image, image 2 1322; a first content field, content field1 1340; and a second content field, content field 2 1342. In practice,the page body 1310 can contain any number of images and content fields,and can include one or more videos, one or more audio presentations, andso on. The page body can include embedded tags, such as tag 1 1330 andtag 2 1332. In the example shown, tag 1 1330 is embedded in image 11320, and tag 2 1332 is embedded in image 2 1322. In embodiments, anynumber of tags can be embedded. Tags can also be embedded in contentfields, in videos, in audio presentations, etc. When a user mouses overa tag or clicks on an object associated with a tag, the tag can beinvoked. For example, when the user mouses over tag 1 1330, tag 1 1330can then be invoked. Invoking tag 1 1330 can include enabling a cameracoupled to a user's device and capturing one or more images of the useras the user views a media presentation (or digital experience). In asimilar manner, when the user mouses over tag 2 1332, tag 2 1332 can beinvoked. Invoking tag 2 1332 can also include enabling the camera andcapturing images of the user. In other embodiments, other actions aretaken based on invocation of the one or more tags. For example, invokingan embedded tag can initiate an analysis technique, post to socialmedia, award the user a coupon or another prize, initiate cognitivestate analysis, perform emotion analysis, and so on.

FIG. 13B shows an example of invoking tags to collect images. As statedabove, a media presentation can include a video, a webpage, and so on. Avideo 1302 can include one or more embedded tags, such as a tag 1360,another tag 1362, a third tag 1364, a fourth tag 1366, and so on. Inpractice, any number of tags can be included in the media presentation.The one or more tags can be invoked during the media presentation. Thecollection of the invoked tags can occur over time, as represented by atimeline 1350. When a tag is encountered in the media presentation, thetag can be invoked. For example, when the tag 1360 is encountered,invoking the tag can enable a camera coupled to a user device and cancapture one or more images of the user viewing the media presentation.Invoking a tag can depend on opt-in by the user. For example, if a userhas agreed to participate in a study by indicating an opt-in, then thecamera coupled to the user's device can be enabled, and one or moreimages of the user can be captured. If the user has not agreed toparticipate in the study and has not indicated an opt-in, then invokingthe tag 1360 does not enable the camera nor capture images of the userduring the media presentation. The user can indicate an opt-in forcertain types of participation, where opting-in can be dependent onspecific content in the media presentation. For example, the user couldopt in to participation in a study of political campaign messages andnot opt in for a particular advertisement study. In this case, tags thatare related to political campaign messages and that enable the cameraand image capture when invoked would be embedded in the mediapresentation. However, tags embedded in the media presentation that arerelated to advertisements would not enable the camera when invoked.Various other situations of tag invocation are possible.

FIG. 14 is a system diagram for an interior of a vehicle 1400. Vehiclemanipulation can be based on cognitive state engineering. Data includingvideo data, facial data, audio data, voice data, physiological data, andso on can be collected from a person in a vehicle. The images or otherdata can be analyzed based on convolutional processing to determinecognitive state. Pixels within an image of a person in a vehicle areanalyzed to identify a facial portion of the person. Facial expressionsare identified based on the facial portion, and the facial expressionsare classified for cognitive response content. The cognitive responsecontent is scored to produce cognitive state information, and thevehicle is manipulated based on communication of the cognitive stateinformation to a component of the vehicle. One or more occupants of avehicle 1410, such as occupants 1420 and 1422, can be observed using amicrophone 1440, one or more cameras 1442, 1444, or 1446, and otheraudio and image capture techniques. The image data can include videodata. The video data and the audio data can include cognitive statedata, where the cognitive state data can include facial data, voicedata, physiological data, and the like. The occupant can be a driver1420 of the vehicle 1410, a passenger 1422 within the vehicle, and soon.

The cameras or imaging devices that can be used to obtain imagesincluding facial data from the occupants of the vehicle 1410 can bepositioned to capture the face of the vehicle operator, the face of avehicle passenger, multiple views of the faces of occupants of thevehicle, and so on. The cameras can be located near a rear-view mirror1414 such as camera 1442, positioned near or on a dashboard 1416 such ascamera 1444, positioned within the dashboard such as camera 1446, and soon. The microphone 1440, or audio capture device, can be positionedwithin the vehicle such that voice data, speech data, non-speechvocalizations, and so on, can be easily collected with minimalbackground noise. In embodiments, additional cameras, imaging devices,microphones, audio capture devices, and so on, can be located throughoutthe vehicle. In further embodiments, each occupant of the vehicle couldhave multiple cameras, microphones, etc., positioned to capture videodata and audio data from that occupant.

The interior of a vehicle 1410 can be a standard vehicle, an autonomousvehicle, a semi-autonomous vehicle, and so on. The vehicle can be asedan or other automobile, a van, a sport utility vehicle (SUV), atruck, a bus, a special purpose vehicle, and the like. The interior ofthe vehicle 1410 can include standard controls such as a steering wheel1436, a throttle control (not shown), a brake 1434, and so on. Theinterior of the vehicle can include other controls 1432 such as controlsfor seats, mirrors, climate controls, audio systems, etc. The controls1432 of the vehicle 1410 can be controlled by a controller 1430. Thecontroller 1430 can control the vehicle 1410 in various manners such asautonomously, semi-autonomously, assertively to a vehicle occupant 1420or 1422, etc. In embodiments, the controller provides vehicle control ormanipulation techniques, assistance, etc. The controller 1430 canreceive instructions via an antenna 1412 or using other wirelesstechniques. The controller 1430 can be preprogrammed to cause thevehicle to follow a specific route. The specific route that the vehicleis programmed to follow can be based on the cognitive state of thevehicle occupant. The specific route can be chosen based on loweststress, least traffic, most scenic view, shortest route, and so on.

FIG. 15 is a timeline with information tracks 1500 relating to cognitivestates. A timeline can show one or more cognitive states that can beexperienced by an individual. The timeline can be based analysisperformed on a chip containing convolutional processing logic within asemiconductor chip. Pixels within an image of a person in a vehicle areanalyzed, where the analysis identifies a facial portion of the person.Facial expressions are identified based on the facial portion, and thefacial expressions are classified for cognitive response content. Thecognitive response content is scored to produce cognitive stateinformation for the person, and the vehicle is manipulated based oncommunication of the cognitive state information to a component of thevehicle. The timeline 1510 with information tracks 1500 relates tovarious cognitive states. A first track 1560 shows events that, inembodiments, are related to use of a computer by the individual. A firstevent 1520 can indicate an action that the individual took (such aslaunching an application); an action initiated by the computer (such asthe presentation of a dialog box); an external event (such as a newglobal positioning system (GPS) coordinate); or another event such asreceiving an email, a phone call, a text message, or any other type ofevent. In some embodiments, a photograph can be used to document anevent or simply to save contextual information in the first track 1560.A second event 1522 can indicate another action or event in a similarmanner. Such events can be used to provide contextual information andcan also include information such as copies of emails, text messages,phone logs, file names, or other information that can prove useful inunderstanding the context of a user's actions. Thus, in embodiments,contextual information is based on one or more of a photograph, anemail, a text message, a phone log, or GPS information.

A second track 1562 can include continuously collected cognitive statedata such as electrodermal activity data 1530. A third track 1564 caninclude voice data 1540. The upper body data, such as upper torso data,can be collected intermittently when the individual is looking toward acamera. The voice data 1540 can include one or more still photographs,videos, or infrared images which can be collected when the user looks inthe direction of the camera. A fourth track 1566 also can include upperbody data that is collected either intermittently or continuously by asecond imaging device. The upper body data 1542 can include one or morestill photographs, videos, infrared images, or abstracted caricatureswhich can be collected when the user looks in the direction of thatcamera. A fifth track 1568 can include facial data that is collectedfrom a third camera, such as the webcam. In the example shown, the fifthtrack 1568 includes first facial data 1544, second facial data 1546, andthird facial data 1548, which can be any type of facial data includingdata that can be used for determining cognitive state information. Anynumber of samples of facial data can be collected in any track. Thecognitive state data from the various tracks can be collectedsimultaneously, collected on one track exclusive of other tracks,collected where cognitive state data overlaps between the tracks, and soon. When cognitive state data from multiple tracks overlap, one track'sdata can take precedence over another track or the data from themultiple tracks can be combined.

Additional tracks, through the n^(th) track 1570, of cognitive statedata of any type can be collected. The additional tracks 1570 can becollected on a continuous or on an intermittent basis. The intermittentbasis can be either occasional or periodic. Analysis can furthercomprise interpolating cognitive state data when the cognitive statedata collected is intermittent, and/or imputing additional cognitivestate data where the cognitive state data is missing. One or moreinterpolated tracks 1572 can be included and can be associated withcognitive state data that is collected on an intermittent basis, such asthe facial data of the fifth track 1568. Interpolated data 1550 andfurther interpolated data 1552 can contain interpolations of the facialdata of the fifth track 1568 for the time periods where no facial datawas collected in that track. Other embodiments interpolate data forperiods where no track includes facial data. In other embodiments,analysis includes interpolating cognitive state analysis when thecognitive state data collected is intermittent.

The cognitive state data, such as the continuous cognitive state data1530 and/or any of the collected voice data 1540 and upper body data1542, and/or facial data 1544, 1546, and 1548, can be tagged. The tagscan include metadata related to the cognitive state data, including, butnot limited to, the device that collected the cognitive state data; theindividual from whom the cognitive state data was collected; the taskbeing performed by the individual; the media being viewed by theindividual; and the location, environ-cognitive conditions, time, date,or any other contextual information. The tags can be used to locatepertinent cognitive state data; for example, the tags can be used toretrieve the cognitive state data from a database. The tags can beincluded with the cognitive state data that is sent over the internet tocloud or web-based storage and/or services. As such, the tags can beused locally on the machine where the cognitive state data was collectedand/or remotely on a remote server or a cloud/web service.

Other tags can be related to the cognitive state data, which is datarelated to, attached to, indicative of, including, containing, etc., thecognitive state. Further embodiments can include tagging the cognitivestate data with sensor data. The sensor data can be obtained from thevehicle occupant along with the video data or the audio data, instead ofthe video data or the audio data, etc. In embodiments, the sensor datacan include one or more of vehicle temperature, outside temperature,time of day, level of daylight, weather conditions, headlightactivation, windshield wiper activation, entertainment center selection,or entertainment center volume. Other sensor data can includephysiological data related to one or more occupants of the vehicle. Thephysiological data can include heart rate, heart rate variability,electrodermal activity, acceleration, and the like. The tags can also berelated to the cognitive state that can be determined by image-basedanalysis of the video, audio, or physiological data, or othertechniques. In embodiments, the tags that can be applied can be based onone or more of drowsiness, fatigue, distraction, impairment, sadness,stress, happiness, anger, frustration, confusion, disappointment,hesitation, cognitive overload, focusing, engagement, attention,boredom, exploration, confidence, trust, delight, disgust, skepticism,doubt, satisfaction, excitement, laughter, calmness, curiosity, humor,depression, envy, sympathy, embarrassment, poignancy, or mirth.

FIG. 16 illustrates example image and audio collection includingmultiple mobile devices. Images, which can include facial or torso data,cognitive state data, audio data, and physiological data, can becollected using multiple mobile devices. The image data can be analyzedusing convolutional processing. The convolutional processing can beapplied to neural network training, where the neural network trainingcan enable deep learning. The deep learning can enable image analysisfor facial evaluation in vehicles. Images that include facial data areobtained for cognitive state analysis. An image of a person in a vehicleis evaluated, where the analysis identifies a facial portion of theperson. The analysis can also identify a body portion such as a torso,an object within the vehicle, and so on. One or more facial expressionsare identified based on the facial portion. The one or more facialexpressions are classified for cognitive response content. The cognitiveresponse content is scored to produce cognitive state information forthe person. The vehicle is manipulated based on communication of thecognitive state information to a component of the vehicle. A cognitivestate can include drowsiness, fatigue, distraction, impairment, sadness,stress, happiness, anger, frustration, confusion, disappointment,hesitation, human perception overload, focusing, engagement, attention,boredom, exploration, confidence, trust, delight, disgust, skepticism,doubt, satisfaction, excitement, laughter, calmness, curiosity, humor,depression, envy, sympathy, embarrassment, poignancy, or mirth.

In the diagram 1600, the multiple mobile devices can be used separatelyor in combination to collect video data, audio data, physiological data,or some or all of video data, audio data, and physiological data, on auser 1610. While one person is shown, the imaging, video data, audiodata, or physiological data can be collected on multiple people. A user1610 can be observed as she or he is performing a task, experiencing anevent, viewing a media presentation, and so on. The user 1610 can beshown one or more media presentations, political presentations, socialmedia, or another form of displayed media. The one or more mediapresentations can be shown to a plurality of people. The mediapresentations can be displayed on an electronic display 1612 or anotherdisplay. The data collected on the user 1610 or on a plurality of userscan be in the form of one or more videos, video frames, and stillimages; one or more audio channels, etc. The plurality of video data andaudio data can be of people who are experiencing different situations.Some example situations can include the user or plurality of users beingexposed to TV programs, movies, video clips, social media, and othersuch media. The situations could also include exposure to media such asadvertisements, political messages, news programs, and so on.

As noted before, video data and audio data can be collected on one ormore users in substantially identical or different situations whileviewing either a single media presentation or a plurality ofpresentations. The data collected on the user 1610 can be analyzed andviewed for a variety of purposes including expression analysis,cognitive state analysis, mental state analysis, emotional stateanalysis, and so on. The electronic display 1612 can be on a laptopcomputer 1620 as shown, a tablet computer 1650, a cell phone 1640, atelevision, a mobile monitor, or any other type of electronic device. Inone embodiment, video data including expression data is collected on amobile device such as a cell phone 1640, a tablet computer 1650, alaptop computer 1620, or a watch 1670. Similarly, the audio dataincluding speech data and non-speech vocalizations can be collected onone or more of the mobile devices. Thus, the multiple sources caninclude at least one mobile device, such as a phone 1640 or a tablet1650, or a wearable device such as a watch 1670 or glasses 1660. Amobile device can include a front-side camera and/or a back-side camerathat can be used to collect expression data. A mobile device can includea microphone, audio transducer, or other audio capture apparatus thatcan be used to capture the speech and non-speech vocalizations. Sourcesof expression data can include a webcam 1622, a phone camera 1642, atablet camera 1652, a wearable camera 1662, and a mobile camera 1630. Awearable camera can comprise various camera devices, such as a watchcamera 1672. Sources of audio data 1682 can include a microphone 1680.

As the user 1610 is monitored, the user might move due to the nature ofthe task, boredom, discomfort, distractions, or for another reason. Asthe user moves, the camera with a view of the user's face can bechanged. Thus, as an example, if the user is looking in a firstdirection, the line of sight 1624 from the webcam 1622 is able toobserve the user's face, but if the user is looking in a seconddirection, the line of sight 1634 from the mobile camera 1630 is able toobserve the user's face. Furthermore, in other embodiments, if the useris looking in a third direction, the line of sight 1644 from the phonecamera 1642 is able to observe the user's face, and if the user islooking in a fourth direction, the line of sight 1654 from the tabletcamera 1652 is able to observe the user's face. If the user is lookingin a fifth direction, the line of sight 1664 from the wearable camera1662, which can be a device such as the glasses 1660 shown which can beworn by another user or an observer, is able to observe the user's face.If the user is looking in a sixth direction, the line of sight 1674 fromthe wearable watch-type device 1670, with a camera 1672 included on thedevice, is able to observe the user's face. In other embodiments, thewearable device is another device, such as an earpiece with a camera, ahelmet or hat with a camera, a clip-on camera attached to clothing, orany other type of wearable device with a camera or other sensor forcollecting expression data. The user 1610 can also use a wearable deviceincluding a camera for gathering contextual information and/orcollecting expression data on other users. Because the user 1610 canmove her or his head, the facial data can be collected intermittentlywhen she or he is looking in a direction of a camera. In some cases,multiple people can be included in the view from one or more cameras,and some embodiments include filtering out faces of one or more otherpeople to determine whether the user 1610 is looking toward a camera.All or some of the expression data can be continuously or sporadicallyavailable from the various devices and other devices.

The captured video data can include facial expressions and can beanalyzed on a computing device such as the video capture device or onanother separate device. The analysis can take place on one of themobile devices discussed above, on a local server, on a remote server,and so on. In embodiments, some of the analysis takes place on themobile device, while other analysis takes place on a server device. Theanalysis of the video data can include the use of a classifier. Thevideo data can be captured using one of the mobile devices discussedabove and sent to a server or another computing device for analysis.However, the captured video data including expressions can also beanalyzed on the device which performed the capturing. The analysis canbe performed on a mobile device where the videos were obtained with themobile device and wherein the mobile device includes one or more of alaptop computer, a tablet, a PDA, a smartphone, a wearable device, andso on. In another embodiment, the analyzing comprises using a classifieron a server or another computing device other than the capturing device.

FIG. 17 is an example showing a convolutional neural network (CNN). Aconvolutional neural network, such as network 1700, can be used forvarious applications. The applications for which the CNN can be used caninclude deep learning, where the deep learning can be applied toconvolutional processing. The convolutional processing supports imageanalysis using a semiconductor processor for facial evaluation invehicles. Pixels within an image of a person in a vehicle are used toanalyze a facial portion of the person. The image can include an imageof a vehicle interior, where the image can be collected using varioustypes of imaging devices. Other data such as audio data or physiologicaldata can also be collected. One or more facial expressions areidentified based on the facial portion. The one or more facialexpressions are classified for cognitive response content. The cognitiveresponse content is scored to produce cognitive state information forthe person. The vehicle is manipulated based on communication of thecognitive state information to a component of the vehicle. A componentof the vehicle can include adjusting vehicle climate control or audioselection, adjusting to traffic or weather, proposing an alternativeroute, and the like. The convolutional neural network can be applied toanalysis tasks such as image analysis, cognitive state analysis, mentalstate analysis, mood analysis, emotional state analysis, and so on. TheCNN can be applied to various tasks such as autonomous vehicle orsemiautonomous vehicle manipulation, vehicle content recommendation, andthe like. When the imaging and other data collected includes cognitivestate data, the cognitive state data can include mental processes, wherethe mental processes can include attention, creativity, memory,perception, problem solving, thinking, use of language, or the like.

Analysis, including cognitive analysis, is a very complex task.Understanding and evaluating moods, emotions, mental states, orcognitive states, requires a nuanced evaluation of facial expressions orother cues generated by people. Cognitive state analysis is important inmany areas such as research, psychology, business, intelligence, lawenforcement, and so on. The understanding of cognitive states can beuseful for a variety of business purposes, such as improving marketinganalysis, assessing the effectiveness of customer service interactionsand retail experiences, and evaluating the consumption of content suchas movies and videos. Identifying points of frustration in a customertransaction can allow a company to address the causes of thefrustration. By streamlining processes, key performance areas such ascustomer satisfaction and customer transaction throughput can beimproved, resulting in increased sales and revenues. In a contentscenario, producing compelling content that achieves the desired effect(e.g. fear, shock, laughter, etc.) can result in increased ticket salesand/or increased advertising revenue. If a movie studio is producing ahorror movie, it is desirable to know if the scary scenes in the movieare achieving the desired effect. By conducting tests in sampleaudiences, and analyzing faces in the audience, a computer-implementedmethod and system can process thousands of faces to assess the cognitivestate at the time of the scary scenes. In many ways, such an analysiscan be more effective than surveys that ask audience members questions,since audience members may consciously or subconsciously change answersbased on peer pressure or other factors. However, spontaneous facialexpressions can be more difficult to conceal. Thus, by analyzing facialexpressions en masse in real time, important information regarding thegeneral cognitive state of the audience can be obtained.

Analysis of facial expressions is also a complex task. Image data, wherethe image data can include facial data, can be analyzed to identify arange of facial expressions. The facial expressions can include a smile,frown, smirk, and so on. The image data and facial data can be processedto identify the facial expressions. The processing can include analysisof expression data, action units, gestures, mental states, cognitivestates, physiological data, and so on. Facial data as contained in theraw video data can include information on one or more of action units,head gestures, smiles, brow furrows, squints, lowered eyebrows, raisedeyebrows, attention, and the like. The action units can be used toidentify smiles, frowns, and other facial indicators of expressions.Gestures can also be identified, and can include a head tilt to theside, a forward lean, a smile, a frown, as well as many other gestures.Other types of data including physiological data can be collected, wherethe physiological data can be obtained using a camera or other imagecapture device, without contacting the person or persons. Respiration,heart rate, heart rate variability, perspiration, temperature, and otherphysiological indicators of cognitive state can be determined byanalyzing the images and video data.

Deep learning is a branch of machine learning which seeks to imitate insoftware the activity which takes place in layers of neurons in theneocortex of the human brain. This imitative activity can enablesoftware to “learn” to recognize and identify patterns in data, wherethe data can include digital forms of images, sounds, and so on. Thedeep learning software is used to simulate the large array of neurons ofthe neocortex. This simulated neocortex, or artificial neural network,can be implemented using mathematical formulas that are evaluated onprocessors. With the ever-increasing capabilities of the processors,increasing numbers of layers of the artificial neural network can beprocessed.

Deep learning applications include processing of image data, audio data,and so on. Image data applications include image recognition, facialrecognition, etc. Image data applications can include differentiatingdogs from cats, identifying different human faces, and the like. Theimage data applications can include identifying cognitive states, moods,mental states, emotional states, and so on, from the facial expressionsof the faces that are identified. Audio data applications can includeanalyzing audio such as ambient room sounds, physiological sounds suchas breathing or coughing, noises made by an individual such as tappingand drumming, voices, and so on. The voice data applications can includeanalyzing a voice for timbre, prosody, vocal register, vocal resonance,pitch, loudness, speech rate, or language content. The voice dataanalysis can be used to determine one or more cognitive states, moods,mental states, emotional states, etc.

The artificial neural network, such as a convolutional neural networkwhich forms the basis for deep learning, is based on layers. The layerscan include an input layer, a convolution layer, a fully connectedlayer, a classification layer, and so on. The input layer can receiveinput data such as image data, where the image data can include avariety of formats including pixel formats. The input layer can thenperform processing tasks such as identifying boundaries of the face,identifying landmarks of the face, extracting features of the face,and/or rotating a face within the plurality of images. The convolutionlayer can represent an artificial neural network such as a convolutionalneural network. A convolutional neural network can contain a pluralityof hidden layers within it. A convolutional layer can reduce the amountof data feeding into a fully connected layer. The fully connected layerprocesses each pixel/data point from the convolutional layer. A lastlayer within the multiple layers can provide output indicative ofcognitive state. The last layer of the convolutional neural network canbe the final classification layer. The output of the finalclassification layer can be indicative of the cognitive states of faceswithin the images that are provided to the input layer.

Deep networks including deep convolutional neural networks can be usedfor facial expression parsing. A first layer of the deep networkincludes multiple nodes, where each node represents a neuron within aneural network. The first layer can receive data from an input layer.The output of the first layer can feed to a second layer, where thelatter layer also includes multiple nodes. A weight can be used toadjust the output of the first layer which is being input to the secondlayer. Some layers in the convolutional neural network can be hiddenlayers. The output of the second layer can feed to a third layer. Thethird layer can also include multiple nodes. A weight can adjust theoutput of the second layer which is being input to the third layer. Thethird layer may be a hidden layer. Outputs of a given layer can be fedto the next layer. Weights adjust the output of one layer as it is fedto the next layer. When the final layer is reached, the output of thefinal layer can be a facial expression, a cognitive state, a mentalstate, a characteristic of a voice, and so on. The facial expression canbe identified using a hidden layer from the one or more hidden layers.The weights can be provided on inputs to the multiple layers toemphasize certain facial features within the face. The convolutionalneural network can be trained to identify facial expressions, voicecharacteristics, etc. The training can include assigning weights toinputs on one or more layers within the multilayered analysis engine.One or more of the weights can be adjusted or updated during training.The assigning weights can be accomplished during a feed-forward passthrough the multilayered neural network. In a feed-forward arrangement,the information moves forward from the input nodes, through the hiddennodes, and on to the output nodes. Additionally, the weights can beupdated during a backpropagation process through the multilayeredanalysis engine.

Returning to the figure, FIG. 17 is an example showing a convolutionalneural network 1700. The convolutional neural network can be used fordeep learning, where the deep learning can be applied to image analysisfor human perception artificial intelligence. The deep learning systemcan be accomplished using a variety of networks. In embodiments, thedeep learning can be performed using a convolution neural network. Othertypes of networks or neural networks can also be used. In otherembodiments, the deep learning can be performed using a recurrent neuralnetwork. The deep learning can accomplish upper torso identification,facial recognition, analysis tasks, etc. The network includes an inputlayer 1710. The input layer 1710 receives image data. The image data canbe input in a variety of formats, such as JPEG, TIFF, BMP, and GIF.Compressed image formats can be decompressed into arrays of pixels,wherein each pixel can include an RGB tuple. The input layer 1710 canthen perform processing such as identifying boundaries of the face,identifying landmarks of the face, extracting features of the face,and/or rotating a face within the plurality of images.

The network includes a collection of intermediate layers 1720. Themultilayered analysis engine can include a convolutional neural network.Thus, the intermediate layers can include a convolution layer 1722. Theconvolution layer 1722 can include multiple sublayers, including hiddenlayers, within it. The output of the convolution layer 1722 feeds into apooling layer 1724. The pooling layer 1724 performs a data reduction,which makes the overall computation more efficient. Thus, the poolinglayer reduces the spatial size of the image representation to reduce thenumber of parameters and computation in the network. In someembodiments, the pooling layer is implemented using filters of size 2×2,applied with a stride of two samples for every depth slice along bothwidth and height, resulting in a reduction of 175-percent of thedownstream node activations. The multilayered analysis engine canfurther include a max pooling layer 1724. Thus, in embodiments, thepooling layer is a max pooling layer, in which the output of the filtersis based on a maximum of the inputs. For example, with a 2×2 filter, theoutput is based on a maximum value from the four input values. In otherembodiments, the pooling layer is an average pooling layer or L2-normpooling layer. Various other pooling schemes are possible.

The intermediate layers can include a Rectified Linear Unit (RELU) layer1726. The output of the pooling layer 1724 can be input to the RELUlayer 1726. In embodiments, the RELU layer implements an activationfunction such as f(x)−max(0,x), thus providing an activation with athreshold at zero. In some embodiments, the RELU layer 1726 is a leakyRELU layer. In this case, instead of the activation function providingzero when x<0, a small negative slope is used, resulting in anactivation function such as f(x)=1(x<0)(αx)+1(x>=0)(x). This can reducethe risk of “dying RELU” syndrome, where portions of the network can be“dead” with nodes/neurons that do not activate across the trainingdataset. The image analysis can comprise training a multilayeredanalysis engine using the plurality of images, wherein the multilayeredanalysis engine can include multiple layers that include one or moreconvolutional layers 1722 and one or more hidden layers, and wherein themultilayered analysis engine can be used for emotional analysis.

The example 1700 includes a fully connected layer 1730. The fullyconnected layer 1730 processes each pixel/data point from the output ofthe collection of intermediate layers 1720. The fully connected layer1730 takes all neurons in the previous layer and connects them to everysingle neuron it has. The output of the fully connected layer 1730provides input to a classification layer 1740. The output of theclassification layer 1740 provides a facial expression and/or cognitivestate as its output. Thus, a multilayered analysis engine such as theone depicted in FIG. 17 processes image data using weights, models theway the human visual cortex performs object recognition and learning,and effectively analyzes image data to infer facial expressions andcognitive states.

Machine learning for generating parameters, analyzing data such asfacial data and audio data, and so on, can be based on a variety ofcomputational techniques. Generally, machine learning can be used forconstructing algorithms and models. The constructed algorithms, whenexecuted, can be used to make a range of predictions relating to data.The predictions can include whether an object in an image is a face, abox, or a puppy; whether a voice is female, male, or robotic; whether amessage is legitimate email or a “spam” message; and so on. The data caninclude unstructured data and can be of large quantity. The algorithmsthat can be generated by machine learning techniques are particularlyuseful to data analysis because the instructions that comprise the dataanalysis technique do not need to be static. Instead, the machinelearning algorithm or model, generated by the machine learningtechnique, can adapt. Adaptation of the learning algorithm can be basedon a range of criteria such as success rate, failure rate, and so on. Asuccessful algorithm is one that can adapt—or learn—as more data ispresented to the algorithm. Initially, an algorithm can be “trained” bypresenting it with a set of known data (supervised learning). Anotherapproach, called unsupervised learning, can be used to identify trendsand patterns within data. Unsupervised learning is not trained usingknown data prior to data analysis.

Reinforced learning is an approach to machine learning that is inspiredby behaviorist psychology. The underlying premise of reinforced learning(also called reinforcement learning) is that software agents can takeactions in an environment. The actions that are taken by the agentsshould maximize a goal such as a “cumulative reward”. A software agentis a computer program that acts on behalf of a user or other program.The software agent is implied to have the authority to act on behalf ofthe user or program. The actions taken are decided by action selectionto determine what to do next. In machine learning, the environment inwhich the agents act can be formulated as a Markov decision process(MDP). The MDPs provide a mathematical framework for modeling ofdecision making in environments where the outcomes can be partly random(stochastic) and partly under the control of the decision maker. Dynamicprogramming techniques can be used for reinforced learning algorithms.Reinforced learning is different from supervised learning in thatcorrect input/output pairs are not presented, and suboptimal actions arenot explicitly corrected. Rather, online or computational performance isthe focus. Online performance includes finding a balance betweenexploration of new (uncharted) territory or spaces and exploitation ofcurrent knowledge. That is, there is a tradeoff between exploration andexploitation.

Machine learning based on reinforced learning adjusts or learns based onlearning an action, a combination of actions, and so on. An outcomeresults from taking an action. Thus, the learning model, algorithm,etc., learns from the outcomes that result from taking the action orcombination of actions. The reinforced learning can include identifyingpositive outcomes, where the positive outcomes are used to adjust thelearning models, algorithms, and so on. A positive outcome can bedependent on a context. When the outcome is based on a mood, emotionalstate, mental state, cognitive state, etc., of an individual, then apositive mood, emotion, mental state, or cognitive state can be used toadjust the model and the algorithm. Positive outcomes can include theperson being more engaged, where engagement is based on affect, theperson spending more time playing an online game or navigating awebpage, the person converting by buying a product or service, and soon. The reinforced learning can be based on exploring a solution spaceand adapting the model, algorithm, etc., which stem from outcomes of theexploration. When positive outcomes are encountered, the positiveoutcomes can be reinforced by changing weighting values within themodel, algorithm, etc. Positive outcomes may result in increasedweighting values. Negative outcomes can also be considered, whereweighting values may be reduced or otherwise adjusted.

FIG. 18 illustrates a bottleneck layer within a deep learningenvironment. A deep learning environment can be based on a neuralnetwork such as a deep neural network. The deep neural network comprisesa plurality of layers such as input layers, output layers, convolutionlayers, activation layers, and so on. The plurality of layers in a deepneural network (DNN) can include a bottleneck layer. The bottlenecklayer can be used for neural network training, where the training can beapplied to analysis such as image analysis. The deep learning networkcan be implemented using a semiconductor chip for convolutionalprocessing. A deep neural network can apply classifiers such as objectclassifiers, image classifiers, facial classifiers, audio classifiers,speech classifiers, physiological classifiers, and so on. Theclassifiers can be learned by analyzing one or more of cognitive states,cognitive load metrics, interaction metrics, etc. Pixels within an imageof a person in a vehicle are analyzed to identify a facial portion ofthe person. Facial expressions associated with the person are identifiedbased on the facial portion. The facial expressions are classified forcognitive response content. The cognitive response content is scored toproduce cognitive state information for the person. Manipulation of thevehicle is enabled based on communication of the cognitive stateinformation to a component of the vehicle.

Layers of a deep neural network can include a bottleneck layer 1800. Abottleneck layer can be used for a variety of applications such asidentification of a facial portion, identification of an upper torso,facial recognition, voice recognition, emotional state recognition, andso on. The deep neural network in which the bottleneck layer is locatedcan include a plurality of layers. The plurality of layers can includean original feature layer 1810. A feature such as an image feature caninclude points, edges, objects, boundaries between and among regions,properties, and so on. The deep neural network can include one or morehidden layers 1820. The one or more hidden layers can include nodes,where the nodes can include nonlinear activation functions and othertechniques. The bottleneck layer can be a layer that learns translationvectors to transform a neutral face to an emotional or expressive face.In some embodiments, the translation vectors can transform a neutralsounding voice to an emotional or expressive voice. Specifically,activations of the bottleneck layer determine how the transformationoccurs. A single bottleneck layer can be trained to transform a neutralface or voice to a different emotional face or voice. In some cases, anindividual bottleneck layer can be trained for a transformation pair. Atruntime, once the user's emotion has been identified and an appropriateresponse to it can be determined (mirrored or complementary), thetrained bottleneck layer can be used to perform the neededtransformation.

The deep neural network can include a bottleneck layer 1830. Thebottleneck layer can include a fewer number of nodes than the one ormore preceding hidden layers. The bottleneck layer can create aconstriction in the deep neural network or other network. The bottlenecklayer can force information that is pertinent to a classification, forexample, into a low dimensional representation. The bottleneck featurescan be extracted using an unsupervised technique. In other embodiments,the bottleneck features can be extracted using a supervised technique.The supervised technique can include training the deep neural networkwith a known dataset. The features can be extracted from an autoencodersuch as a variational autoencoder, a generative autoencoder, and so on.The deep neural network can include hidden layers 1840. The number ofthe hidden layers can include zero hidden layers, one hidden layer, aplurality of hidden layers, and so on. The hidden layers following thebottleneck layer can include more nodes than the bottleneck layer. Thedeep neural network can include a classification layer 1850. Theclassification layer can be used to identify the points, edges, objects,boundaries, and so on, described above. The classification layer can beused to identify cognitive states, mental states, emotional states,moods, and the like. The output of the final classification layer can beindicative of the emotional states of faces within the images, where theimages can be processed using the deep neural network.

FIG. 19 shows data collection including devices and locations 1900.Data, including imaging, facial data, video data, audio data, and physiodata can be obtained for analysis such as image analysis using asemiconductor processor. The semiconductor can be used for facialevaluation of one or more persons in vehicles. The imaging, audio,physio, and other data can be obtained from multiple devices, vehicles,and locations. Pixels within an image of a person in a vehicle areanalyzed, where the analysis identifies a facial portion of the person.One or more facial expressions are identified based on the facialportion. The one or more facial expressions are classified for cognitiveresponse content. The cognitive response content is evaluated to producecognitive state information for the person. The vehicle is manipulatedbased on communication of the cognitive state information to a componentof the vehicle.

The multiple mobile devices, vehicles, and locations 1900 can be usedseparately or in combination to collect imaging, video data, audio data,physio data, etc., on a user 1910. The imaging can include video data,where the video data can include upper torso data. Other data such asaudio data, physiological data, and so on, can be collected on the user.While one person is shown, the video data, or other data, can becollected on multiple people. A user 1910 can be observed as she or heis performing a task, experiencing an event, viewing a mediapresentation, and so on. The user 1910 can be shown one or more mediapresentations, political presentations, social media, or another form ofdisplayed media. The one or more media presentations can be shown to aplurality of people. The media presentations can be displayed on anelectronic display coupled to a client device. The data collected on theuser 1910 or on a plurality of users can be in the form of one or morevideos, video frames, still images, etc. The plurality of videos can beof people who are experiencing different situations. Some examplesituations can include the user or plurality of users being exposed toTV programs, movies, video clips, social media, social sharing, andother such media. The situations could also include exposure to mediasuch as advertisements, political messages, news programs, and so on. Asnoted before, video data can be collected on one or more users insubstantially identical or different situations and viewing either asingle media presentation or a plurality of presentations. The datacollected on the user 1910 can be analyzed and viewed for a variety ofpurposes including body position or body language analysis, expressionanalysis, mental state analysis, cognitive state analysis, and so on.The electronic display can be on a smartphone 1920 as shown, a tabletcomputer 1930, a personal digital assistant, a television, a mobilemonitor, or any other type of electronic device. In one embodiment,expression data is collected on a mobile device such as a cell phone1920, a tablet computer 1930, a laptop computer, or a watch. Thus, themultiple sources can include at least one mobile device, such as a phone1920 or a tablet 1930, or a wearable device such as a watch or glasses(not shown). A mobile device can include a front-side camera and/or aback-side camera that can be used to collect expression data. Sources ofexpression data can include a webcam, a phone camera, a tablet camera, awearable camera, and a mobile camera. A wearable camera can comprisevarious camera devices, such as a watch camera. In addition to usingclient devices for data collection from the user 1910, data can becollected in a house 1940 using a web camera or the like; in a vehicle1950 using a web camera, client device, etc.; by a social robot 1960,and so on.

As the user 1910 is monitored, the user 1910 might move due to thenature of the task, boredom, discomfort, distractions, or for anotherreason. As the user moves, the camera with a view of the user's face canbe changed. Thus, as an example, if the user 1910 is looking in a firstdirection, the line of sight 1922 from the smartphone 1920 is able toobserve the user's face, but if the user is looking in a seconddirection, the line of sight 1932 from the tablet 1930 is able toobserve the user's face. Furthermore, in other embodiments, if the useris looking in a third direction, the line of sight 1942 from a camera inthe house 1940 is able to observe the user's face, and if the user islooking in a fourth direction, the line of sight 1952 from the camera inthe vehicle 1950 is able to observe the user's face. If the user islooking in a fifth direction, the line of sight 1962 from the socialrobot 1960 is able to observe the user's face. If the user is looking ina sixth direction, a line of sight from a wearable watch-type device,with a camera included on the device, is able to observe the user'sface. In other embodiments, the wearable device is another device, suchas an earpiece with a camera, a helmet or hat with a camera, a clip-oncamera attached to clothing, or any other type of wearable device with acamera or other sensor for collecting expression data. The user 1910 canalso use a wearable device including a camera for gathering contextualinformation and/or collecting expression data on other users. Becausethe user 1910 can move her or his head, the facial data can be collectedintermittently when she or he is looking in a direction of a camera. Insome cases, multiple people can be included in the view from one or morecameras, and some embodiments include filtering out faces of one or moreother people to determine whether the user 1910 is looking toward acamera. All or some of the expression data can be continuously orsporadically available from the various devices and other devices.

The captured video data can include cognitive content, such as facialexpressions, etc., and can be transferred over a network 1970. Thenetwork can include the Internet or other computer network. Thesmartphone 1920 can share video using a link 1924, the tablet 1930 usinga link 1934, the house 1940 using a link 1944, the vehicle 1950 using alink 1954, and the social robot 1960 using a link 1964. The links 1924,1934, 1944, 1954, and 1964 can be wired, wireless, and hybrid links. Thecaptured video data, including facial expressions, can be analyzed on acognitive state analysis machine 1980, on a computing device such as thevideo capture device, or on another separate device. The analysis couldtake place on one of the mobile devices discussed above, on a localserver, on a remote server, and so on. In embodiments, some of theanalysis takes place on the mobile device, while other analysis takesplace on a server device. The analysis of the video data can include theuse of a classifier. The video data can be captured using one of themobile devices discussed above and sent to a server or another computingdevice for analysis. However, the captured video data includingexpressions can also be analyzed on the device which performed thecapturing. The analysis can be performed on a mobile device where thevideos were obtained with the mobile device and wherein the mobiledevice includes one or more of a laptop computer, a tablet, a PDA, asmartphone, a wearable device, and so on. In another embodiment, theanalyzing comprises using a classifier on a server or another computingdevice different from the capture device. The analysis data from thecognitive state analysis engine can be processed by a cognitive stateindicator 1990. The cognitive state indicator 1990 can indicatecognitive states, mental states, moods, emotions, etc. In embodiments,the cognitive state can include drowsiness, fatigue, distraction,impairment, sadness, stress, happiness, anger, frustration, confusion,disappointment, hesitation, cognitive overload, focusing, engagement,attention, boredom, exploration, confidence, trust, delight, disgust,skepticism, doubt, satisfaction, excitement, laughter, calmness,curiosity, humor, depression, envy, sympathy, embarrassment, poignancy,or mirth.

FIG. 20 is a system for image and cognitive state analysis using aconvolutional processing device. The convolutional processing deviceuses semiconductor-based logic to perform or augment the neededanalysis. An example system 2000 is shown for cognitive state datacollection and analysis. The analyzed cognitive state data is used forvehicle manipulation. The system 2000 can include a memory which storesinstructions and one or more processors attached to the memory, whereinthe one or more processors, when executing the instructions which arestored, are configured to: analyze pixels within an image of a person ina vehicle, wherein the analysis identifies a facial portion of theperson; identify one or more facial expressions based on the facialportion; classify the one or more facial expressions for cognitiveresponse content; evaluate the cognitive response content to producecognitive state information for the person; and manipulate the vehiclebased on communication of the cognitive state information to a componentof the vehicle. In embodiments, the device updates a cognitive stateprofile the person associated with the facial portion. The cognitivestate profile summarizes the cognitive state information of theindividual. In some embodiments, an additional facial portion from animage of an additional person within the vehicle is evaluated,identified, classified, and scored to produce additional cognitive stateinformation for the additional person.

The system 2000 can provide an apparatus for analysis comprising: adevice containing convolutional processing logic encoded in asemiconductor chip comprising: evaluation logic trained to analyzepixels within an image of a person in a vehicle, wherein the analysisidentifies a facial portion of the person; identification logic trainedto identify one or more facial expressions based on the facial portion;classifying logic trained to classify the one or more facial expressionsfor cognitive response content; scoring logic trained to evaluate thecognitive response content to produce cognitive state information forthe person; and interface logic that enables manipulation of the vehiclebased on communication of the cognitive state information to a componentof the vehicle.

The system 2000 can provide a processor-implemented method for analysiscomprising: using a device containing convolutional processing logicencoded in a semiconductor chip to perform: analyzing pixels within animage of a person in a vehicle, wherein the analysis identifies a facialportion of the person; identifying one or more facial expressions basedon the facial portion; classifying the one or more facial expressionsfor cognitive response content; evaluating the cognitive responsecontent to produce cognitive state information for the person; andmanipulating the vehicle based on communication of the cognitive stateinformation to a component of the vehicle.

The system 2000 can include one or more video data collection machines2020 linked to an analysis machine 2040 and a manipulation machine 2050via a network 2010 or another computer network. The network can be wiredor wireless, a computer network such as the Internet, and so on. Videodata 2060 can be transferred to the analysis machine 2040 through thenetwork 2010. The example video data collection machine 2020 showncomprises one or more processors 2024 coupled to a memory 2026 which canstore and retrieve instructions, a display 2022, a camera 2028, and amicrophone 2030. The camera 2028 can include a webcam, a video camera, astill camera, a thermal imager, a CCD device, a phone camera, athree-dimensional camera, a depth camera, a light field camera, multiplewebcams used to show different views of a person, or any other type ofimage capture technique that can allow captured data to be used in anelectronic system. The microphone can include any audio capture devicethat can enable captured audio data to be used by the electronic system.The memory 2026 can be used for storing instructions, video data on aplurality of people, audio data from the plurality of people, one ormore classifiers, and so on. The display 2022 can be any electronicdisplay, including but not limited to, a computer display, a laptopscreen, a net-book screen, a tablet computer screen, a smartphonedisplay, a mobile device display, a remote with a display, a television,a projector, or the like.

The analysis machine 2040 can include one or more processors 2044coupled to a memory 2046 which can store and retrieve instructions, andcan also include a display 2042. The analysis machine 2040 can receivethe video data 2060 and can analyze pixels within an image of a personin a vehicle. The analysis can identify a facial portion of the person,a portion of a torso of the person, and so on. The analysis thatidentifies the facial portion or other portion of the person can beaccomplished using one or more classifiers. The one or more classifierscan be stored within the analysis machine, loaded into the analysismachine, provided by a user of the analysis machine, and so on. Theanalysis machine 2040 can use video data received from the video datacollection machine 2020 to produce feature data 2062. In someembodiments, the analysis machine 2040 receives video data from aplurality of video data collection machines, aggregates the video data,processes the video data or the aggregated video data, and so on.

The manipulation machine 2050 can include one or more processors 2054coupled to a memory 2056 which can store and retrieve instructions anddata, and can also include a display 2052. The manipulation of a vehiclebased on manipulation communication data 2064 can occur on themanipulation machine 2050 or on a machine or platform different from themanipulation machine 2050. In embodiments, the manipulation of thevehicle based on the manipulation communication data occurs on the videodata collection machine 2020 or on the analysis machine 2040. As shownin the system 2000, the manipulation machine 2050 can receivemanipulation communication data 2064 via the network 2010, the Internet,or another network, from the video data collection machine 2020, fromthe analysis machine 2040, or from both. The manipulation of thevehicle, which can include alerts, warnings, displays, cognitive stateindications, and so on, can include a visual rendering on a display orany other appropriate display format.

The system 2000 can include a computer program product embodied in anon-transitory computer readable medium for image analysis, the computerprogram product comprising: code for executing on a device containing aconvolutional processing logic encoded in a semiconductor chipcomprising: evaluation logic trained to analyze pixels within an imageof a person in a vehicle, wherein the analysis identifies a facialportion of the person; identification logic trained to identify one ormore facial expressions based on the facial portion; classifying logictrained to classify the one or more facial expressions for cognitiveresponse content; scoring logic trained to evaluate the cognitiveresponse content to produce cognitive state information for the person;and interface logic that enables manipulation of the vehicle based oncommunication of the cognitive state information to a component of thevehicle.

Each of the above methods may be executed on one or more processors onone or more computer systems. Embodiments may include various forms ofdistributed computing, client/server computing, and cloud-basedcomputing. Further, it will be understood that the depicted steps orboxes contained in this disclosure's flow charts are solely illustrativeand explanatory. The steps may be modified, omitted, repeated, orre-ordered without departing from the scope of this disclosure. Further,each step may contain one or more sub-steps. While the foregoingdrawings and description set forth functional aspects of the disclosedsystems, no particular implementation or arrangement of software and/orhardware should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. All such arrangements ofsoftware and/or hardware are intended to fall within the scope of thisdisclosure.

The block diagrams and flowchart illustrations depict methods,apparatus, systems, and computer program products. The elements andcombinations of elements in the block diagrams and flow diagrams, showfunctions, steps, or groups of steps of the methods, apparatus, systems,computer program products and/or computer-implemented methods. Any andall such functions—generally referred to herein as a “circuit,”“module,” or “system”—may be implemented by computer programinstructions, by special-purpose hardware-based computer systems, bycombinations of special purpose hardware and computer instructions, bycombinations of general purpose hardware and computer instructions, andso on.

A programmable apparatus which executes any of the above-mentionedcomputer program products or computer-implemented methods may includeone or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors, programmabledevices, programmable gate arrays, programmable array logic, memorydevices, application specific integrated circuits, or the like. Each maybe suitably employed or configured to process computer programinstructions, execute computer logic, store computer data, and so on.

It will be understood that a computer may include a computer programproduct from a computer-readable storage medium and that this medium maybe internal or external, removable and replaceable, or fixed. Inaddition, a computer may include a Basic Input/Output System (BIOS),firmware, an operating system, a database, or the like that may include,interface with, or support the software and hardware described herein.

Embodiments of the present invention are limited to neither conventionalcomputer applications nor the programmable apparatus that run them. Toillustrate: the embodiments of the presently claimed invention couldinclude an optical computer, quantum computer, analog computer, or thelike. A computer program may be loaded onto a computer to produce aparticular machine that may perform any and all of the depictedfunctions. This particular machine provides a means for carrying out anyand all of the depicted functions.

Any combination of one or more computer readable media may be utilizedincluding but not limited to: a non-transitory computer readable mediumfor storage; an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor computer readable storage medium or anysuitable combination of the foregoing; a portable computer diskette; ahard disk; a random access memory (RAM); a read-only memory (ROM), anerasable programmable read-only memory (EPROM, Flash, MRAM, FeRAM, orphase change memory); an optical fiber; a portable compact disc; anoptical storage device; a magnetic storage device; or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

It will be appreciated that computer program instructions may includecomputer executable code. A variety of languages for expressing computerprogram instructions may include without limitation C, C++, Java,JavaScript™, ActionScript™, assembly language, Lisp, Perl, Tcl, Python,Ruby, hardware description languages, database programming languages,functional programming languages, imperative programming languages, andso on. In embodiments, computer program instructions may be stored,compiled, or interpreted to run on a computer, a programmable dataprocessing apparatus, a heterogeneous combination of processors orprocessor architectures, and so on. Without limitation, embodiments ofthe present invention may take the form of web-based computer software,which includes client/server software, software-as-a-service,peer-to-peer software, or the like.

In embodiments, a computer may enable execution of computer programinstructions including multiple programs or threads. The multipleprograms or threads may be processed approximately simultaneously toenhance utilization of the processor and to facilitate substantiallysimultaneous functions. By way of implementation, any and all methods,program codes, program instructions, and the like described herein maybe implemented in one or more threads which may in turn spawn otherthreads, which may themselves have priorities associated with them. Insome embodiments, a computer may process these threads based on priorityor other order.

Unless explicitly stated or otherwise clear from the context, the verbs“execute” and “process” may be used interchangeably to indicate execute,process, interpret, compile, assemble, link, load, or a combination ofthe foregoing. Therefore, embodiments that execute or process computerprogram instructions, computer-executable code, or the like may act uponthe instructions or code in any and all of the ways described. Further,the method steps shown are intended to include any suitable method ofcausing one or more parties or entities to perform the steps. Theparties performing a step, or portion of a step, need not be locatedwithin a particular geographic location or country boundary. Forinstance, if an entity located within the United States causes a methodstep, or portion thereof, to be performed outside of the United Statesthen the method is considered to be performed in the United States byvirtue of the causal entity.

While the invention has been disclosed in connection with preferredembodiments shown and described in detail, various modifications andimprovements thereon will become apparent to those skilled in the art.Accordingly, the foregoing examples should not limit the spirit andscope of the present invention; rather it should be understood in thebroadest sense allowable by law.

What is claimed is:
 1. An apparatus for analysis comprising: a devicecontaining convolutional processing logic encoded in a semiconductorchip comprising: evaluation logic trained to analyze pixels within animage of a person in a vehicle, wherein the analysis identifies a facialportion of the person; identification logic trained to identify one ormore facial expressions based on the facial portion; classifying logictrained to classify the one or more facial expressions for cognitiveresponse content; scoring logic trained to evaluate the cognitiveresponse content to produce cognitive state information for the person;and interface logic that enables manipulation of the vehicle based oncommunication of the cognitive state information to a component of thevehicle.
 2. The apparatus of claim 1 further comprising categorizationlogic that updates a cognitive state profile of an individual associatedwith the facial portion.
 3. The apparatus of claim 2 wherein thecognitive state profile summarizes the cognitive state information ofthe individual.
 4. The apparatus of claim 3 wherein the cognitive stateprofile is based on cognitive state event temporal signatures.
 5. Theapparatus of claim 1 wherein an additional facial portion from an imageof an additional person within the vehicle is evaluated, identified,classified, and scored to produce additional cognitive state informationfor the additional person.
 6. The apparatus of claim 1 wherein thecognitive state information is used to communicate one or more ofdrowsiness, fatigue, distraction, sadness, stress, happiness, anger,frustration, confusion, disappointment, hesitation, cognitive overload,focusing, engagement, attention, boredom, exploration, confidence,trust, delight, disgust, skepticism, doubt, satisfaction, excitement,laughter, calmness, curiosity, humor, depression, envy, sympathy,embarrassment, poignancy, or mirth.
 7. The apparatus of claim 1 furthercomprising logic for augmenting the cognitive state information based onaudio data collected from within the vehicle, wherein the audio data iscollected contemporaneously with the image.
 8. The apparatus of claim 7wherein the audio data includes voice data.
 9. The apparatus of claim 1wherein the manipulation of the vehicle includes a locking outoperation, a recommending a break for an occupant, a recommending adifferent route for the vehicle, a recommending how far to drive, aresponding to traffic, an adjusting of seats, mirrors, climate control,lighting, music, audio stimuli, or interior temperature, a brakeactivation, or a steering control.
 10. The apparatus of claim 1 furthercomprising logic for tagging the cognitive state information with sensordata received from the vehicle.
 11. The apparatus of claim 1 wherein thecognitive state information that was analyzed is based on intermittentoccurrences of the facial portion within a series of images.
 12. Theapparatus of claim 1 wherein a series of images is supplied to thedevice and wherein the series of images is sourced from a video stream.13. The apparatus of claim 12 further comprising tracking logic trainedfor tracking the facial portion and identifying that the facial portionis no longer within images from the video stream.
 14. The apparatus ofclaim 13 wherein the tracking logic identifies that a face has left theimages from the video stream.
 15. The apparatus of claim 14 wherein thetracking logic identifies that the face has returned to the images fromthe video stream and associates information previously collected aboutthe face from before the face left the video stream.
 16. The apparatusof claim 1 wherein the cognitive response content includes facialexpressions.
 17. The apparatus of claim 1 wherein the classifier logicis further trained to identify a gender, age, or ethnicity for the face.18. The apparatus of claim 17 wherein the gender, age, or ethnicity isprovided with an associated probability. 19-20. (canceled)
 21. Theapparatus in claim 1 wherein the cognitive state information is used bya software application running on a processor coupled to the device. 22.The apparatus in claim 1 wherein the device sends one or more images toa web service for external classification based on the cognitive stateinformation.
 23. The apparatus in claim 1 wherein the device furtherperforms smoothing of the cognitive state information.
 24. The apparatusin claim 1 wherein the device further performs image correction for theimage including one or more of lighting correction, contrast correction,near infrared lighting correction, or noise filtering.
 25. The apparatusin claim 1 wherein physiological information is gleaned from a videocontaining the image.
 26. (canceled)
 27. A computer program productembodied in a non-transitory computer readable medium for imageanalysis, the computer program product comprising: code for executing ona device containing a convolutional processing logic encoded in asemiconductor chip comprising: evaluation logic trained to analyzepixels within an image of a person in a vehicle, wherein the analysisidentifies a facial portion of the person; identification logic trainedto identify one or more facial expressions based on the facial portion;classifying logic trained to classify the one or more facial expressionsfor cognitive response content; scoring logic trained to evaluate thecognitive response content to produce cognitive state information forthe person; and interface logic that enables manipulation of the vehiclebased on communication of the cognitive state information to a componentof the vehicle.
 28. A processor-implemented method for analysiscomprising: using a device containing convolutional processing logicencoded in a semiconductor chip to perform: analyzing pixels within animage of a person in a vehicle, wherein the analysis identifies a facialportion of the person; identifying one or more facial expressions basedon the facial portion; classifying the one or more facial expressionsfor cognitive response content; evaluating the cognitive responsecontent to produce cognitive state information for the person; andmanipulating the vehicle based on communication of the cognitive stateinformation to a component of the vehicle.